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Xie GB, Yu Y, Lin ZY, Chen RB, Xie JH, Liu ZG. 4 mC site recognition algorithm based on pruned pre-trained DNABert-Pruning model and fused artificial feature encoding. Anal Biochem 2024; 689:115492. [PMID: 38458307 DOI: 10.1016/j.ab.2024.115492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 02/21/2024] [Indexed: 03/10/2024]
Abstract
DNA 4 mC plays a crucial role in the genetic expression process of organisms. However, existing deep learning algorithms have shortcomings in the ability to represent DNA sequence features. In this paper, we propose a 4 mC site identification algorithm, DNABert-4mC, based on a fusion of the pruned pre-training DNABert-Pruning model and artificial feature encoding to identify 4 mC sites. The algorithm prunes and compresses the DNABert model, resulting in the pruned pre-training model DNABert-Pruning. This model reduces the number of parameters and removes redundancy from output features, yielding more precise feature representations while upholding accuracy.Simultaneously, the algorithm constructs an artificial feature encoding module to assist the DNABert-Pruning model in feature representation, effectively supplementing the information that is missing from the pre-trained features. The algorithm also introduces the AFF-4mC fusion strategy, which combines artificial feature encoding with the DNABert-Pruning model, to improve the feature representation capability of DNA sequences in multi-semantic spaces and better extract 4 mC sites and the distribution of nucleotide importance within the sequence. In experiments on six independent test sets, the DNABert-4mC algorithm achieved an average AUC value of 93.81%, outperforming seven other advanced algorithms with improvements of 2.05%, 5.02%, 11.32%, 5.90%, 12.02%, 2.42% and 2.34%, respectively.
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Affiliation(s)
- Guo-Bo Xie
- Guangdong University of Technology, Guangzhou, 510000, China
| | - Yi Yu
- Guangdong University of Technology, Guangzhou, 510000, China
| | - Zhi-Yi Lin
- Guangdong University of Technology, Guangzhou, 510000, China.
| | - Rui-Bin Chen
- Guangdong University of Technology, Guangzhou, 510000, China
| | - Jian-Hui Xie
- Guangdong University of Technology, Guangzhou, 510000, China
| | - Zhen-Guo Liu
- Department of Thoracic Surgery, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhongshan 2nd Road, Guangzhou, 510080, China.
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2
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Cao L, Wu C, Luo G, Guo C, Zheng A. Online biomedical named entities recognition by data and knowledge-driven model. Artif Intell Med 2024; 150:102813. [PMID: 38553155 DOI: 10.1016/j.artmed.2024.102813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 12/15/2023] [Accepted: 02/12/2024] [Indexed: 04/02/2024]
Abstract
Named entity recognition (NER) is an important task for the natural language processing of biomedical text. Currently, most NER studies standardized biomedical text, but NER for unstandardized biomedical text draws less attention from researchers. Named entities in online biomedical text exist with errors and polymorphisms, which negatively impact NER models' performance and impede support from knowledge representation methods. In this paper, we propose a neural network method that can effectively recognize entities in unstandardized online medical/health text. We introduce a new pre-training scheme that uses large-scale online question-answering pairs to enhance transformers' model capacity on online biomedical text. Moreover, we supply models with knowledge representations from a knowledge base called multi-channel knowledge labels, and this method overcomes the restriction from languages, like Chinese, that require word segmentation tools to represent knowledge. Our model outperforms other baseline methods significantly in experiments on a dataset for Chinese online medical entity recognition and achieves state-of-the-art results.
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Affiliation(s)
- Lulu Cao
- Department of Rheumatology and Immunology, Peking University People's Hospital, 100044, China
| | - Chaochen Wu
- Renmin University of China, Beijing, 100872, China.
| | - Guan Luo
- State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation, Chinese Academy of Sciences, China.
| | - Chao Guo
- Department of Cardiology, Fuwai Hospital CAMS and PUMC, Beijing, 100037, China
| | - Anni Zheng
- State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation, Chinese Academy of Sciences, China
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3
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Xu K, You K, Zhu B, Feng M, Feng D, Yang C. Masked Modeling-Based Ultrasound Image Classification via Self-Supervised Learning. IEEE Open J Eng Med Biol 2024; 5:226-237. [PMID: 38606402 PMCID: PMC11008806 DOI: 10.1109/ojemb.2024.3374966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 02/21/2024] [Accepted: 03/05/2024] [Indexed: 04/13/2024] Open
Abstract
Recently, deep learning-based methods have emerged as the preferred approach for ultrasound data analysis. However, these methods often require large-scale annotated datasets for training deep models, which are not readily available in practical scenarios. Additionally, the presence of speckle noise and other imaging artifacts can introduce numerous hard examples for ultrasound data classification. In this paper, drawing inspiration from self-supervised learning techniques, we present a pre-training method based on mask modeling specifically designed for ultrasound data. Our study investigates three different mask modeling strategies: random masking, vertical masking, and horizontal masking. By employing these strategies, our pre-training approach aims to predict the masked portion of the ultrasound images. Notably, our method does not rely on externally labeled data, allowing us to extract representative features without the need for human annotation. Consequently, we can leverage unlabeled datasets for pre-training. Furthermore, to address the challenges posed by hard samples in ultrasound data, we propose a novel hard sample mining strategy. To evaluate the effectiveness of our proposed method, we conduct experiments on two datasets. The experimental results demonstrate that our approach outperforms other state-of-the-art methods in ultrasound image classification. This indicates the superiority of our pre-training method and its ability to extract discriminative features from ultrasound data, even in the presence of hard examples.
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Affiliation(s)
- Kele Xu
- National University of Defense TechnologyChangsha410073China
| | - Kang You
- Shanghai Jiao Tong UniversityShanghai200240China
| | - Boqing Zhu
- National University of Defense TechnologyChangsha410073China
| | - Ming Feng
- TongJi UniversityShanghai200070China
| | - Dawei Feng
- National University of Defense TechnologyChangsha410073China
| | - Cheng Yang
- National University of Defense TechnologyChangsha410073China
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4
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Han J, Kwon Y, Choi YS, Kang S. Improving chemical reaction yield prediction using pre-trained graph neural networks. J Cheminform 2024; 16:25. [PMID: 38429787 PMCID: PMC10905905 DOI: 10.1186/s13321-024-00818-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Accepted: 02/19/2024] [Indexed: 03/03/2024] Open
Abstract
Graph neural networks (GNNs) have proven to be effective in the prediction of chemical reaction yields. However, their performance tends to deteriorate when they are trained using an insufficient training dataset in terms of quantity or diversity. A promising solution to alleviate this issue is to pre-train a GNN on a large-scale molecular database. In this study, we investigate the effectiveness of GNN pre-training in chemical reaction yield prediction. We present a novel GNN pre-training method for performance improvement.Given a molecular database consisting of a large number of molecules, we calculate molecular descriptors for each molecule and reduce the dimensionality of these descriptors by applying principal component analysis. We define a pre-text task by assigning a vector of principal component scores as the pseudo-label to each molecule in the database. A GNN is then pre-trained to perform the pre-text task of predicting the pseudo-label for the input molecule. For chemical reaction yield prediction, a prediction model is initialized using the pre-trained GNN and then fine-tuned with the training dataset containing chemical reactions and their yields. We demonstrate the effectiveness of the proposed method through experimental evaluation on benchmark datasets.
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Affiliation(s)
- Jongmin Han
- Department of Industrial Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Republic of Korea
| | - Youngchun Kwon
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon, Republic of Korea
| | - Youn-Suk Choi
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon, Republic of Korea.
| | - Seokho Kang
- Department of Industrial Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Republic of Korea.
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5
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Zheng L, Shi S, Lu M, Fang P, Pan Z, Zhang H, Zhou Z, Zhang H, Mou M, Huang S, Tao L, Xia W, Li H, Zeng Z, Zhang S, Chen Y, Li Z, Zhu F. AnnoPRO: a strategy for protein function annotation based on multi-scale protein representation and a hybrid deep learning of dual-path encoding. Genome Biol 2024; 25:41. [PMID: 38303023 PMCID: PMC10832132 DOI: 10.1186/s13059-024-03166-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 01/05/2024] [Indexed: 02/03/2024] Open
Abstract
Protein function annotation has been one of the longstanding issues in biological sciences, and various computational methods have been developed. However, the existing methods suffer from a serious long-tail problem, with a large number of GO families containing few annotated proteins. Herein, an innovative strategy named AnnoPRO was therefore constructed by enabling sequence-based multi-scale protein representation, dual-path protein encoding using pre-training, and function annotation by long short-term memory-based decoding. A variety of case studies based on different benchmarks were conducted, which confirmed the superior performance of AnnoPRO among available methods. Source code and models have been made freely available at: https://github.com/idrblab/AnnoPRO and https://zenodo.org/records/10012272.
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Affiliation(s)
- Lingyan Zheng
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
- Industry Solutions Research and Development, Alibaba Cloud Computing, Hangzhou, 330110, China
| | - Shuiyang Shi
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Mingkun Lu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Pan Fang
- Industry Solutions Research and Development, Alibaba Cloud Computing, Hangzhou, 330110, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
| | - Ziqi Pan
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Hongning Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Zhimeng Zhou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Hanyu Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Shijie Huang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, Hangzhou Normal University, Hangzhou, 311121, China
| | - Weiqi Xia
- Pharmaceutical Department, Zhejiang Provincial People's Hospital, Hangzhou, 310014, China
| | - Honglin Li
- School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Zhenyu Zeng
- Industry Solutions Research and Development, Alibaba Cloud Computing, Hangzhou, 330110, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
| | - Shun Zhang
- Industry Solutions Research and Development, Alibaba Cloud Computing, Hangzhou, 330110, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
| | - Yuzong Chen
- State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, The Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, China
| | - Zhaorong Li
- Industry Solutions Research and Development, Alibaba Cloud Computing, Hangzhou, 330110, China.
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China.
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China.
- Industry Solutions Research and Development, Alibaba Cloud Computing, Hangzhou, 330110, China.
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China.
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6
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Ma J, Kong D, Wu F, Bao L, Yuan J, Liu Y. Densely connected convolutional networks for ultrasound image based lesion segmentation. Comput Biol Med 2024; 168:107725. [PMID: 38006827 DOI: 10.1016/j.compbiomed.2023.107725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 11/03/2023] [Accepted: 11/15/2023] [Indexed: 11/27/2023]
Abstract
Delineating lesion boundaries play a central role in diagnosing thyroid and breast cancers, making related therapy plans and evaluating therapeutic effects. However, it is often time-consuming and error-prone with limited reproducibility to manually annotate low-quality ultrasound (US) images, given high speckle noises, heterogeneous appearances, ambiguous boundaries etc., especially for nodular lesions with huge intra-class variance. It is hence appreciative but challenging for accurate lesion segmentations from US images in clinical practices. In this study, we propose a new densely connected convolutional network (called MDenseNet) architecture to automatically segment nodular lesions from 2D US images, which is first pre-trained over ImageNet database (called PMDenseNet) and then retrained upon the given US image datasets. Moreover, we also designed a deep MDenseNet with pre-training strategy (PDMDenseNet) for segmentation of thyroid and breast nodules by adding a dense block to increase the depth of our MDenseNet. Extensive experiments demonstrate that the proposed MDenseNet-based method can accurately extract multiple nodular lesions, with even complex shapes, from input thyroid and breast US images. Moreover, additional experiments show that the introduced MDenseNet-based method also outperforms three state-of-the-art convolutional neural networks in terms of accuracy and reproducibility. Meanwhile, promising results in nodular lesion segmentation from thyroid and breast US images illustrate its great potential in many other clinical segmentation tasks.
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Affiliation(s)
- Jinlian Ma
- School of Integrated Circuits, Shandong University, Jinan 250101, China; Shenzhen Research Institute of Shandong University, A301 Virtual University Park in South District of Shenzhen, China; State Key Lab of CAD&CG, College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China.
| | - Dexing Kong
- School of Mathematical Sciences, Zhejiang University, Hangzhou 310027, China
| | - Fa Wu
- School of Mathematical Sciences, Zhejiang University, Hangzhou 310027, China
| | - Lingyun Bao
- Department of Ultrasound, Hangzhou First Peoples Hospital, Zhejiang University, Hangzhou, China
| | - Jing Yuan
- School of Mathematics and Statistics, Xidian University, China
| | - Yusheng Liu
- State Key Lab of CAD&CG, College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
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7
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Liu L, Zhang Y, Sun L. Medimatrix: innovative pre-training of grayscale images for rheumatoid arthritis diagnosis revolutionises medical image classification. Health Inf Sci Syst 2023; 11:44. [PMID: 37771395 PMCID: PMC10522544 DOI: 10.1007/s13755-023-00246-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 09/08/2023] [Indexed: 09/30/2023] Open
Abstract
Efficient and accurate medical image classification (MIC) methods face two major challenges: (1) high similarity between images of different disease classes; and (2) generating large medical image datasets for training deep neural networks is challenging due to privacy restrictions and the need for expert ground truth annotations. In this paper, we introduce a novel deep learning method called pre-training grayscale images with supervised learning for MIC (MediMatrix). Instead of pre-training on color ImageNet, our approach uses MediMatrix on grayscale ImageNet. To improve the performance of the network, we introduce ShuffleAttention (SA), a self-attention mechanism. By combining SA with the multiple residual structure (ResSA block) and replacing short-cut connections with dense residual connections between corresponding layers (densepath), our network can dynamically adjust channel attention weights and receive image inputs of different sizes, resulting in improved feature representation and better discrimination of similarities between different categories. MediMatrix effectively classifies X-ray images of rheumatoid arthritis (RA), enabling efficient screening without the need for expert analysis or invasive testing. Through extensive experiments, we demonstrate the superiority of MediMatrix over state-of-the-art methods and that color is not critical for rich natural image classification. Our results highlight the potential of computer-aided diagnosis combined with MediMatrix as a valuable screening tool for early detection and intervention in RA.
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Affiliation(s)
- Linchen Liu
- Department of Rheumatology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, 210009 China
| | - Yiyang Zhang
- Department of Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, 210044 China
| | - Le Sun
- Department of Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, 210044 China
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8
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Pellegrini C, Navab N, Kazi A. Unsupervised pre-training of graph transformers on patient population graphs. Med Image Anal 2023; 89:102895. [PMID: 37473609 DOI: 10.1016/j.media.2023.102895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 07/06/2023] [Accepted: 07/07/2023] [Indexed: 07/22/2023]
Abstract
Pre-training has shown success in different areas of machine learning, such as Computer Vision, Natural Language Processing (NLP), and medical imaging. However, it has not been fully explored for clinical data analysis. An immense amount of clinical records are recorded, but still, data and labels can be scarce for data collected in small hospitals or dealing with rare diseases. In such scenarios, pre-training on a larger set of unlabeled clinical data could improve performance. In this paper, we propose novel unsupervised pre-training techniques designed for heterogeneous, multi-modal clinical data for patient outcome prediction inspired by masked language modeling (MLM), by leveraging graph deep learning over population graphs. To this end, we further propose a graph-transformer-based network, designed to handle heterogeneous clinical data. By combining masking-based pre-training with a transformer-based network, we translate the success of masking-based pre-training in other domains to heterogeneous clinical data. We show the benefit of our pre-training method in a self-supervised and a transfer learning setting, utilizing three medical datasets TADPOLE, MIMIC-III, and a Sepsis Prediction Dataset. We find that our proposed pre-training methods help in modeling the data at a patient and population level and improve performance in different fine-tuning tasks on all datasets.
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Affiliation(s)
- Chantal Pellegrini
- Computer Aided Medical Procedures, Technical University of Munich, Munich, Germany.
| | - Nassir Navab
- Computer Aided Medical Procedures, Technical University of Munich, Munich, Germany; Computer Aided Medical Procedures, Johns Hopkins University, Baltimore, USA
| | - Anees Kazi
- Computer Aided Medical Procedures, Technical University of Munich, Munich, Germany; Massachusetts General Hospital, Harvard Medical School, Cambridge, MA, USA
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9
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Luo N, Zhong X, Su L, Cheng Z, Ma W, Hao P. Artificial intelligence-assisted dermatology diagnosis: From unimodal to multimodal. Comput Biol Med 2023; 165:107413. [PMID: 37703714 DOI: 10.1016/j.compbiomed.2023.107413] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 08/02/2023] [Accepted: 08/28/2023] [Indexed: 09/15/2023]
Abstract
Artificial Intelligence (AI) is progressively permeating medicine, notably in the realm of assisted diagnosis. However, the traditional unimodal AI models, reliant on large volumes of accurately labeled data and single data type usage, prove insufficient to assist dermatological diagnosis. Augmenting these models with text data from patient narratives, laboratory reports, and image data from skin lesions, dermoscopy, and pathologies could significantly enhance their diagnostic capacity. Large-scale pre-training multimodal models offer a promising solution, exploiting the burgeoning reservoir of clinical data and amalgamating various data types. This paper delves into unimodal models' methodologies, applications, and shortcomings while exploring how multimodal models can enhance accuracy and reliability. Furthermore, integrating cutting-edge technologies like federated learning and multi-party privacy computing with AI can substantially mitigate patient privacy concerns in dermatological datasets and further fosters a move towards high-precision self-diagnosis. Diagnostic systems underpinned by large-scale pre-training multimodal models can facilitate dermatology physicians in formulating effective diagnostic and treatment strategies and herald a transformative era in healthcare.
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Affiliation(s)
- Nan Luo
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
| | - Xiaojing Zhong
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
| | - Luxin Su
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
| | - Zilin Cheng
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
| | - Wenyi Ma
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
| | - Pingsheng Hao
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
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10
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Tun YL, Nguyen MNH, Thwal CM, Choi J, Hong CS. Contrastive encoder pre-training-based clustered federated learning for heterogeneous data. Neural Netw 2023; 165:689-704. [PMID: 37385023 DOI: 10.1016/j.neunet.2023.06.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 05/26/2023] [Accepted: 06/05/2023] [Indexed: 07/01/2023]
Abstract
Federated learning (FL) is a promising approach that enables distributed clients to collaboratively train a global model while preserving their data privacy. However, FL often suffers from data heterogeneity problems, which can significantly affect its performance. To address this, clustered federated learning (CFL) has been proposed to construct personalized models for different client clusters. One effective client clustering strategy is to allow clients to choose their own local models from a model pool based on their performance. However, without pre-trained model parameters, such a strategy is prone to clustering failure, in which all clients choose the same model. Unfortunately, collecting a large amount of labeled data for pre-training can be costly and impractical in distributed environments. To overcome this challenge, we leverage self-supervised contrastive learning to exploit unlabeled data for the pre-training of FL systems. Together, self-supervised pre-training and client clustering can be crucial components for tackling the data heterogeneity issues of FL. Leveraging these two crucial strategies, we propose contrastive pre-training-based clustered federated learning (CP-CFL) to improve the model convergence and overall performance of FL systems. In this work, we demonstrate the effectiveness of CP-CFL through extensive experiments in heterogeneous FL settings, and present various interesting observations.
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Affiliation(s)
- Ye Lin Tun
- Department of Computer Science and Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do 17104, South Korea.
| | - Minh N H Nguyen
- Vietnam - Korea University of Information and Communication Technology, Danang, Viet Nam.
| | - Chu Myaet Thwal
- Department of Computer Science and Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do 17104, South Korea.
| | - Jinwoo Choi
- Department of Computer Science and Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do 17104, South Korea.
| | - Choong Seon Hong
- Department of Computer Science and Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do 17104, South Korea.
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11
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Cai H, Lan L, Zhang J, Zhang X, Luo Z. SiamDF: Tracking training data-free siamese tracker. Neural Netw 2023; 165:705-720. [PMID: 37385024 DOI: 10.1016/j.neunet.2023.06.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 03/19/2023] [Accepted: 06/06/2023] [Indexed: 07/01/2023]
Abstract
Much progress has been made in siamese tracking, primarily benefiting from increasing huge training data. However, very little attention has been really paid to the role of huge training data in learning an effective siamese tracker. In this study, we undertake an in-depth analysis of this issue from a novel optimization perspective, and observe that training data is particularly adept at background suppression, thereby refining target representation. Inspired by this insight, we present a data-free siamese tracking algorithm named SiamDF, which requires only a pre-trained backbone and no further fine-tuning on additional training data. Particularly, to suppress background distractors, we separately improve two branches of siamese tracking by retaining the pure target region as target input with the removal of template background, and by exploring an efficient inverse transformation to maintain the constant aspect ratio of target state in search region. Besides, we further promote the center displacement prediction of the entire backbone by eliminating its spatial stride deviations caused by convolution-like quantification operations. Our experimental results on several popular benchmarks demonstrate that SiamDF, free from both offline fine-tuning and online update, achieves impressive performance compared to well-established unsupervised and supervised tracking methods.
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Affiliation(s)
- Huayue Cai
- Institute for Quantum & State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha, China
| | - Long Lan
- Institute for Quantum & State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha, China
| | - Jing Zhang
- School of Computer Science, University of Sydney, Sydney, Australia
| | - Xiang Zhang
- Institute for Quantum & State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha, China; Laboratory of Digitizing Software for Frontier Equipment, National University of Defense Technology, Changsha, China.
| | - Zhigang Luo
- Science and Technology on Parallel and Distributed Laboratory, College of Computer, National University of Defense Technology, Changsha, China
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12
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Vendrow E, Schonfeld E. Understanding transfer learning for chest radiograph clinical report generation with modified transformer architectures. Heliyon 2023; 9:e17968. [PMID: 37519756 PMCID: PMC10372225 DOI: 10.1016/j.heliyon.2023.e17968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 07/02/2023] [Accepted: 07/04/2023] [Indexed: 08/01/2023] Open
Abstract
The image captioning task is increasingly prevalent in artificial intelligence applications for medicine. One important application is clinical report generation from chest radiographs. The clinical writing of unstructured reports is time consuming and error-prone. An automated system would improve standardization, error reduction, time consumption, and medical accessibility. In this paper we demonstrate the importance of domain specific pre-training and propose a modified transformer architecture for the medical image captioning task. To accomplish this, we train a series of modified transformers to generate clinical reports from chest radiograph image input. These modified transformers include: a meshed-memory augmented transformer architecture with visual extractor using ImageNet pre-trained weights, a meshed-memory augmented transformer architecture with visual extractor using CheXpert pre-trained weights, and a meshed-memory augmented transformer whose encoder is passed the concatenated embeddings using both ImageNet pre-trained weights and CheXpert pre-trained weights. We use BLEU(1-4), ROUGE-L, CIDEr, and the clinical CheXbert F1 scores to validate our models and demonstrate competitive scores with state of the art models. We provide evidence that ImageNet pre-training is ill-suited for the medical image captioning task, especially for less frequent conditions (e.g.: enlarged cardiomediastinum, lung lesion, pneumothorax). Furthermore, we demonstrate that the double feature model improves performance for specific medical conditions (edema, consolidation, pneumothorax, support devices) and overall CheXbert F1 score, and should be further developed in future work. Such a double feature model, including both ImageNet pre-training as well as domain specific pre-training, could be used in a wide range of image captioning models in medicine.
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Affiliation(s)
- Edward Vendrow
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 50 Vassar St, Cambridge, MA, United States of America
| | - Ethan Schonfeld
- School of Medicine, Stanford University, 291 Campus Drive, Stanford, CA, United States of America
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13
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Zhu S, Zheng W, Pang H. CPAE: Contrastive predictive autoencoder for unsupervised pre-training in health status prediction. Comput Methods Programs Biomed 2023; 234:107484. [PMID: 37030137 DOI: 10.1016/j.cmpb.2023.107484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 02/20/2023] [Accepted: 03/12/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND AND OBJECTIVE Fully-supervised learning approaches have shown promising results in some health status prediction tasks using Electronic Health Records (EHRs). These traditional approaches rely on sufficient labeled data to learn from. However, in practice, acquiring large-scaled labeled medical data for various prediction tasks is often not feasible. Thus, it is of great interest to utilize contrastive pre-training to leverage the unlabeled information. METHODS In this work, we propose a novel data-efficient framework, contrastive predictive autoencoder (CPAE), to first learn without labels from the EHR data in the pre-training process, and then fine-tune on the downstream tasks. Our framework comprises of two parts: (i) a contrastive learning process, inherited from contrastive predictive coding (CPC), which aims to extract global slow-varying features, and (ii) a reconstruction process, which forces the encoder to capture local features. We also introduce the attention mechanism in one variant of our framework to balance the above two processes. RESULTS Experiments on real-world EHR dataset verify the effectiveness of our proposed framework on two downstream tasks (i.e., in-hospital mortality prediction and length-of-stay prediction), compared to their supervised counterparts, the CPC model, and other baseline models. CONCLUSIONS By comprising of both contrastive learning components and reconstruction components, CPAE aims to extract both global slow-varying information and local transient information. The best results on two downstream tasks are all achieved by CPAE. The variant AtCPAE is particularly superior when fine-tuned on very small training data. Further work may incorporate techniques of multi-task learning to optimize the pre-training process of CPAEs. Moreover, this work is based on the benchmark MIMIC-III dataset which only includes 17 variables. Future work may extend to a larger number of variables.
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Affiliation(s)
- Shuying Zhu
- Li Ka Shing Faculty of Medicine, the University of Hong Kong, Hong Kong SAR, China.
| | - Weizhong Zheng
- Li Ka Shing Faculty of Medicine, the University of Hong Kong, Hong Kong SAR, China.
| | - Herbert Pang
- Li Ka Shing Faculty of Medicine, the University of Hong Kong, Hong Kong SAR, China; Department of Biostatistics and Bioinformatics, Duke University School of Medicine, NC, USA.
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14
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Aaron Li Y, Han C, Jiang X, Mesgarani N. PHONEME-LEVEL BERT FOR ENHANCED PROSODY OF TEXT-TO-SPEECH WITH GRAPHEME PREDICTIONS. Proc IEEE Int Conf Acoust Speech Signal Process 2023; 2023:10.1109/icassp49357.2023.10097074. [PMID: 37577179 PMCID: PMC10417533 DOI: 10.1109/icassp49357.2023.10097074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Large-scale pre-trained language models have been shown to be helpful in improving the naturalness of text-to-speech (TTS) models by enabling them to produce more naturalistic prosodic patterns. However, these models are usually word-level or sup-phoneme-level and jointly trained with phonemes, making them inefficient for the downstream TTS task where only phonemes are needed. In this work, we propose a phoneme-level BERT (PL-BERT) with a pretext task of predicting the corresponding graphemes along with the regular masked phoneme predictions. Subjective evaluations show that our phoneme-level BERT encoder has significantly improved the mean opinion scores (MOS) of rated naturalness of synthesized speech compared with the state-of-the-art (SOTA) StyleTTS baseline on out-of-distribution (OOD) texts.
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Affiliation(s)
| | - Cong Han
- Department of Electrical Engineering, Columbia University, USA
| | - Xilin Jiang
- Department of Electrical Engineering, Columbia University, USA
| | - Nima Mesgarani
- Department of Electrical Engineering, Columbia University, USA
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15
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Luo H, Shan W, Chen C, Ding P, Luo L. Improving language model of human genome for DNA-protein binding prediction based on task-specific pre-training. Interdiscip Sci 2023; 15:32-43. [PMID: 36136096 DOI: 10.1007/s12539-022-00537-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 08/30/2022] [Accepted: 09/07/2022] [Indexed: 11/27/2022]
Abstract
The DNA-protein binding plays a pivotal role in regulating gene expression and evolution, and computational identification of DNA-protein has drawn more and more attention in bioinformatics. Recently, variants of BERT are also used to capture the semantic information of DNA sequences for predicting DNA-protein bindings. In this study, we leverage a task-specific pre-training strategy on BERT using large-scale multi-source DNA-protein binding data and present TFBert. TFBert treats DNA sequences as natural sentences and k-mer nucleotides as words. It can effectively extract upstream and downstream nucleotide context information by pre-training the 690 unlabeled ChIP-seq datasets. Experiments show that the pre-trained model can achieve promising performance on every single dataset in the 690 ChIP-seq datasets after simple fine tuning, especially on small datasets. The average AUC is 94.7%, outperforming existing popular methods. In conclusion, this study provides a variant of BERT based on pre-training and achieved state-of-the-art results in predicting DNA-protein bindings. We believe that TFBert can provide insights into other biological sequence classification problems.
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Affiliation(s)
- Hanyu Luo
- School of Computer Science, University of South China, Hengyang, Hunan, 421001, People's Republic of China
| | - Wenyu Shan
- School of Computer Science, University of South China, Hengyang, Hunan, 421001, People's Republic of China
| | - Cheng Chen
- School of Computer Science, University of South China, Hengyang, Hunan, 421001, People's Republic of China
| | - Pingjian Ding
- School of Computer Science, University of South China, Hengyang, Hunan, 421001, People's Republic of China
| | - Lingyun Luo
- School of Computer Science, University of South China, Hengyang, Hunan, 421001, People's Republic of China. .,Hunan Medical Big Data International Science and Technology Innovation Cooperation Base, Hengyang, Hunan, 421001, People's Republic of China.
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16
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Wanyan T, Lin M, Klang E, Menon KM, Gulamali FF, Azad A, Zhang Y, Ding Y, Wang Z, Wang F, Glicksberg B, Peng Y. Supervised Pretraining through Contrastive Categorical Positive Samplings to Improve COVID-19 Mortality Prediction. ACM BCB 2022; 2022:9. [PMID: 35960866 PMCID: PMC9365529 DOI: 10.1145/3535508.3545541] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Clinical EHR data is naturally heterogeneous, where it contains abundant sub-phenotype. Such diversity creates challenges for outcome prediction using a machine learning model since it leads to high intra-class variance. To address this issue, we propose a supervised pre-training model with a unique embedded k-nearest-neighbor positive sampling strategy. We demonstrate the enhanced performance value of this framework theoretically and show that it yields highly competitive experimental results in predicting patient mortality in real-world COVID-19 EHR data with a total of over 7,000 patients admitted to a large, urban health system. Our method achieves a better AUROC prediction score of 0.872, which outperforms the alternative pre-training models and traditional machine learning methods. Additionally, our method performs much better when the training data size is small (345 training instances).
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Affiliation(s)
- Tingyi Wanyan
- Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Mingquan Lin
- Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Eyal Klang
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | | | - Ariful Azad
- Intelligent Systems Engineering, Indiana University, Bloomington, Bloomington, IN, USA
| | - Yiye Zhang
- Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Ying Ding
- School of Information, University of Texus Austin, Austin, TX, USA
| | - Zhangyang Wang
- Electrical and Computer Engineering, University of Texus Austin, Austin, TX, USA
| | - Fei Wang
- Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | | | - Yifan Peng
- Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
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17
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R S, Thaseen IS, M V, M D, M A, R M, Mahendran A, Alnumay W, Chatterjee P. An efficient hardware architecture based on an ensemble of deep learning models for COVID -19 prediction. Sustain Cities Soc 2022; 80:103713. [PMID: 35136715 PMCID: PMC8812126 DOI: 10.1016/j.scs.2022.103713] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 01/21/2022] [Accepted: 01/21/2022] [Indexed: 05/17/2023]
Abstract
Deep learning models demonstrate superior performance in image classification problems. COVID-19 image classification is developed using single deep learning models. In this paper, an efficient hardware architecture based on an ensemble deep learning model is built to identify the COVID-19 using chest X-ray (CXR) records. Five deep learning models namely ResNet, fitness, IRCNN (Inception Recurrent Convolutional Neural Network), effectiveness, and Fitnet are ensembled for fine-tuning and enhancing the performance of the COVID-19 identification; these models are chosen as they individually perform better in other applications. Experimental analysis shows that the accuracy, precision, recall, and F1 for COVID-19 detection are 0.99,0.98,0.98, and 0.98 respectively. An application-specific hardware architecture incorporates the pipeline, parallel processing, reusability of computational resources by carefully exploiting the data flow and resource availability. The processing element (PE) and the CNN architecture are modeled using Verilog, simulated, and synthesized using cadence with Taiwan Semiconductor Manufacturing Co Ltd (TSMC) 90 nm tech file. The simulated results show a 40% reduction in the latency and number of clock cycles. The computations and power consumptions are minimized by designing the PE as a data-aware unit. Thus, the proposed architecture is best suited for Covid-19 prediction and diagnosis.
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Affiliation(s)
- Sakthivel R
- School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - I Sumaiya Thaseen
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Vanitha M
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Deepa M
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Angulakshmi M
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Mangayarkarasi R
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Anand Mahendran
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
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18
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Liang G, Ganesh H, Steffe D, Liu L, Jacobs N, Zhang J. Development of CNN models for the enteral feeding tube positioning assessment on a small scale data set. BMC Med Imaging 2022; 22:52. [PMID: 35317725 PMCID: PMC8939093 DOI: 10.1186/s12880-022-00766-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 02/17/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Enteral nutrition through feeding tubes serves as the primary method of nutritional supplementation for patients unable to feed themselves. Plain radiographs are routinely used to confirm the position of the Nasoenteric feeding tubes the following insertion and before the commencement of tube feeds. Convolutional neural networks (CNNs) have shown encouraging results in assisting the tube positioning assessment. However, robust CNNs are often trained using large amounts of manually annotated data, which challenges applying CNNs on enteral feeding tube positioning assessment. METHOD We build a CNN model for feeding tube positioning assessment by pre-training the model under a weakly supervised fashion on large quantities of radiographs. Since most of the model was pre-trained, a small amount of labeled data is needed when fine-tuning the model for tube positioning assessment. We demonstrate the proposed method using a small dataset with 175 radiographs. RESULT The experimental result shows that the proposed model improves the area under the receiver operating characteristic curve (AUC) by up to 35.71% , from 0.56 to 0.76, and 14.49% on the accuracy, from 0.69 to 0.79 when compared with the no pre-trained method. The proposed method also has up to 40% less error when estimating its prediction confidence. CONCLUSION Our evaluation results show that the proposed model has a high prediction accuracy and a more accurate estimated prediction confidence when compared to the no pre-trained model and other baseline models. The proposed method can be potentially used for assessing the enteral tube positioning. It also provides a strong baseline for future studies.
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Affiliation(s)
| | | | | | | | | | - Jie Zhang
- University of Kentucky, Lexington, KY, USA.
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19
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Abstract
Background For the past decades, benefitting from the rapid growth of protein sequence data in public databases, a lot of machine learning methods have been developed to predict physicochemical properties or functions of proteins using amino acid sequence features. However, the prediction performance often suffers from the lack of labeled data. In recent years, pre-training methods have been widely studied to address the small-sample issue in computer vision and natural language processing fields, while specific pre-training techniques for protein sequences are few. Results In this paper, we propose a pre-training platform for representing protein sequences, called ProtPlat, which uses the Pfam database to train a three-layer neural network, and then uses specific training data from downstream tasks to fine-tune the model. ProtPlat can learn good representations for amino acids, and at the same time achieve efficient classification. We conduct experiments on three protein classification tasks, including the identification of type III secreted effectors, the prediction of subcellular localization, and the recognition of signal peptides. The experimental results show that the pre-training can enhance model performance effectively and ProtPlat is competitive to the state-of-the-art predictors, especially for small datasets. We implement the ProtPlat platform as a web service (https://compbio.sjtu.edu.cn/protplat) that is accessible to the public. Conclusions To enhance the feature representation of protein amino acid sequences and improve the performance of sequence-based classification tasks, we develop ProtPlat, a general platform for the pre-training of protein sequences, which is featured by a large-scale supervised training based on Pfam database and an efficient learning model, FastText. The experimental results of three downstream classification tasks demonstrate the efficacy of ProtPlat. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04604-2.
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Affiliation(s)
- Yuan Jin
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai, 200240, China
| | - Yang Yang
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai, 200240, China.
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20
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Zhang H, Hu D, Duan H, Li S, Wu N, Lu X. A novel deep learning approach to extract Chinese clinical entities for lung cancer screening and staging. BMC Med Inform Decis Mak 2021; 21:214. [PMID: 34330277 PMCID: PMC8323233 DOI: 10.1186/s12911-021-01575-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 07/07/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Computed tomography (CT) reports record a large volume of valuable information about patients' conditions and the interpretations of radiology images from radiologists, which can be used for clinical decision-making and further academic study. However, the free-text nature of clinical reports is a critical barrier to use this data more effectively. In this study, we investigate a novel deep learning method to extract entities from Chinese CT reports for lung cancer screening and TNM staging. METHODS The proposed approach presents a new named entity recognition algorithm, namely the BERT-based-BiLSTM-Transformer network (BERT-BTN) with pre-training, to extract clinical entities for lung cancer screening and staging. Specifically, instead of traditional word embedding methods, BERT is applied to learn the deep semantic representations of characters. Following the long short-term memory layer, a Transformer layer is added to capture the global dependencies between characters. Besides, pre-training technique is employed to alleviate the problem of insufficient labeled data. RESULTS We verify the effectiveness of the proposed approach on a clinical dataset containing 359 CT reports collected from the Department of Thoracic Surgery II of Peking University Cancer Hospital. The experimental results show that the proposed approach achieves an 85.96% macro-F1 score under exact match scheme, which improves the performance by 1.38%, 1.84%, 3.81%,4.29%,5.12%,5.29% and 8.84% compared to BERT-BTN, BERT-LSTM, BERT-fine-tune, BERT-Transformer, FastText-BTN, FastText-BiLSTM and FastText-Transformer, respectively. CONCLUSIONS In this study, we developed a novel deep learning method, i.e., BERT-BTN with pre-training, to extract the clinical entities from Chinese CT reports. The experimental results indicate that the proposed approach can efficiently recognize various clinical entities about lung cancer screening and staging, which shows the potential for further clinical decision-making and academic research.
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Affiliation(s)
- Huanyao Zhang
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Zheda Road, Hangzhou, China
- Key Laboratory for Biomedical Engineering, Ministry of Education, Zheda Road, Hangzhou, China
| | - Danqing Hu
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Zheda Road, Hangzhou, China
- Key Laboratory for Biomedical Engineering, Ministry of Education, Zheda Road, Hangzhou, China
| | - Huilong Duan
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Zheda Road, Hangzhou, China
- Key Laboratory for Biomedical Engineering, Ministry of Education, Zheda Road, Hangzhou, China
| | - Shaolei Li
- Department of Thoracic Surgery II, Peking University Cancer Hospital & Institute, Beijing, China
| | - Nan Wu
- Department of Thoracic Surgery II, Peking University Cancer Hospital & Institute, Beijing, China
| | - Xudong Lu
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Zheda Road, Hangzhou, China
- Key Laboratory for Biomedical Engineering, Ministry of Education, Zheda Road, Hangzhou, China
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21
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Dligach D, Afshar M, Miller T. Pre-training phenotyping classifiers. J Biomed Inform 2020; 113:103626. [PMID: 33259943 DOI: 10.1016/j.jbi.2020.103626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Revised: 09/09/2020] [Accepted: 11/14/2020] [Indexed: 11/17/2022]
Abstract
Recent transformer-based pre-trained language models have become a de facto standard for many text classification tasks. Nevertheless, their utility in the clinical domain, where classification is often performed at encounter or patient level, is still uncertain due to the limitation on the maximum length of input. In this work, we introduce a self-supervised method for pre-training that relies on a masked token objective and is free from the limitation on the maximum input length. We compare the proposed method with supervised pre-training that uses billing codes as a source of supervision. We evaluate the proposed method on one publicly-available and three in-house datasets using the standard evaluation metrics such as the area under the ROC curve and F1 score. We find that, surprisingly, even though self-supervised pre-training performs slightly worse than supervised, it still preserves most of the gains from pre-training.
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Affiliation(s)
- Dmitriy Dligach
- Loyola University Chicago, Department of Computer Science, Chicago, IL, United States.
| | - Majid Afshar
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin Madison, Madison, WI, United States.
| | - Timothy Miller
- Computational Health Informatics Program (CHIP), Boston Children's Hospital and Harvard Medical School, Boston, MA, United States.
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22
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Ong BT, Sugiura K, Zettsu K. Dynamically pre-trained deep recurrent neural networks using environmental monitoring data for predicting PM 2.5. Neural Comput Appl 2015; 27:1553-1566. [PMID: 27418719 PMCID: PMC4920860 DOI: 10.1007/s00521-015-1955-3] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2014] [Accepted: 06/05/2015] [Indexed: 12/24/2022]
Abstract
Fine particulate matter (\documentclass[12pt]{minimal}
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\begin{document}$$\hbox {PM}_{2.5}$$\end{document}PM2.5) has a considerable impact on human health, the environment and climate change. It is estimated that with better predictions, US$9 billion can be saved over a 10-year period in the USA (State of the science fact sheet air quality. http://www.noaa.gov/factsheets/new, 2012). Therefore, it is crucial to keep developing models and systems that can accurately predict the concentration of major air pollutants. In this paper, our target is to predict \documentclass[12pt]{minimal}
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\begin{document}$$\hbox {PM}_{2.5}$$\end{document}PM2.5 concentration in Japan using environmental monitoring data obtained from physical sensors with improved accuracy over the currently employed prediction models. To do so, we propose a deep recurrent neural network (DRNN) that is enhanced with a novel pre-training method using auto-encoder especially designed for time series prediction. Additionally, sensors selection is performed within DRNN without harming the accuracy of the predictions by taking advantage of the sparsity found in the network. The numerical experiments show that DRNN with our proposed pre-training method is superior than when using a canonical and a state-of-the-art auto-encoder training method when applied to time series prediction. The experiments confirm that when compared against the \documentclass[12pt]{minimal}
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\begin{document}$$\hbox {PM}_{2.5}$$\end{document}PM2.5 prediction system VENUS (National Institute for Environmental Studies. Visual Atmospheric Environment Utility System. http://envgis5.nies.go.jp/osenyosoku/, 2014), our technique improves the accuracy of \documentclass[12pt]{minimal}
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\begin{document}$$\hbox {PM}_{2.5}$$\end{document}PM2.5 concentration level predictions that are being reported in Japan.
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Affiliation(s)
- Bun Theang Ong
- Information Services Platform Laboratory, Universal Communication Research Institute, National Institute of Information and Communications Technology, 3-5 Hikaridai, Seika-cho, Kyoto, Soraku-gun 619-0289 Japan
| | - Komei Sugiura
- Information Services Platform Laboratory, Universal Communication Research Institute, National Institute of Information and Communications Technology, 3-5 Hikaridai, Seika-cho, Kyoto, Soraku-gun 619-0289 Japan
| | - Koji Zettsu
- Information Services Platform Laboratory, Universal Communication Research Institute, National Institute of Information and Communications Technology, 3-5 Hikaridai, Seika-cho, Kyoto, Soraku-gun 619-0289 Japan
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