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Alzoubi I, Bao G, Zheng Y, Wang X, Graeber MB. Artificial intelligence techniques for neuropathological diagnostics and research. Neuropathology 2022. [PMID: 36443935 DOI: 10.1111/neup.12880] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 10/17/2022] [Accepted: 10/23/2022] [Indexed: 12/03/2022]
Abstract
Artificial intelligence (AI) research began in theoretical neurophysiology, and the resulting classical paper on the McCulloch-Pitts mathematical neuron was written in a psychiatry department almost 80 years ago. However, the application of AI in digital neuropathology is still in its infancy. Rapid progress is now being made, which prompted this article. Human brain diseases represent distinct system states that fall outside the normal spectrum. Many differ not only in functional but also in structural terms, and the morphology of abnormal nervous tissue forms the traditional basis of neuropathological disease classifications. However, only a few countries have the medical specialty of neuropathology, and, given the sheer number of newly developed histological tools that can be applied to the study of brain diseases, a tremendous shortage of qualified hands and eyes at the microscope is obvious. Similarly, in neuroanatomy, human observers no longer have the capacity to process the vast amounts of connectomics data. Therefore, it is reasonable to assume that advances in AI technology and, especially, whole-slide image (WSI) analysis will greatly aid neuropathological practice. In this paper, we discuss machine learning (ML) techniques that are important for understanding WSI analysis, such as traditional ML and deep learning, introduce a recently developed neuropathological AI termed PathoFusion, and present thoughts on some of the challenges that must be overcome before the full potential of AI in digital neuropathology can be realized.
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Affiliation(s)
- Islam Alzoubi
- School of Computer Science The University of Sydney Sydney New South Wales Australia
| | - Guoqing Bao
- School of Computer Science The University of Sydney Sydney New South Wales Australia
| | - Yuqi Zheng
- Ken Parker Brain Tumour Research Laboratories Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney Camperdown New South Wales Australia
| | - Xiuying Wang
- School of Computer Science The University of Sydney Sydney New South Wales Australia
| | - Manuel B. Graeber
- Ken Parker Brain Tumour Research Laboratories Brain and Mind Centre, Faculty of Medicine and Health, University of Sydney Camperdown New South Wales Australia
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Deep learning for colon cancer histopathological images analysis. Comput Biol Med 2021; 136:104730. [PMID: 34375901 DOI: 10.1016/j.compbiomed.2021.104730] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 07/28/2021] [Accepted: 08/01/2021] [Indexed: 10/20/2022]
Abstract
Nowadays, digital pathology plays a major role in the diagnosis and prognosis of tumours. Unfortunately, existing methods remain limited when faced with the high resolution and size of Whole Slide Images (WSIs) coupled with the lack of richly annotated datasets. Regarding the ability of the Deep Learning (DL) methods to cope with the large scale applications, such models seem like an appealing solution for tissue classification and segmentation in histopathological images. This paper focuses on the use of DL architectures to classify and highlight colon cancer regions in a sparsely annotated histopathological data context. First, we review and compare state-of-the-art Convolutional Neural networks (CNN) including the AlexNet, vgg, ResNet, DenseNet and Inception models. To cope with the shortage of rich WSI datasets, we have resorted to the use of transfer learning techniques. This strategy comes with the hallmark of relying on a large size computer vision dataset (ImageNet) to train the network and generate a rich collection of learnt features. The testing and evaluation of such models on our AiCOLO colon cancer dataset ensure accurate patch-level classification results reaching up to 96.98% accuracy rate with ResNet. The CNN models have also been tested and evaluated with the CRC-5000, nct-crc-he-100k and merged datasets. ResNet respectively achieves 96.77%, 99.76% and 99.98% for the three publicly available datasets. Then, we present a pixel-wise segmentation strategy for colon cancer WSIs through the use of both UNet and SegNet models. We introduce a multi-step training strategy as a remedy for the sparse annotation of histopathological images. UNet and SegNet are used and tested in different training scenarios including data augmentation and transfer learning and ensure up to 76.18% and 81.22% accuracy rates. Besides, we test our training strategy and models on the CRC-5000, nct-crc-he-100k and Warwick datasets. Respective accuracy rates of 98.66%, 99.12% and 78.39% were achieved by SegNet. Finally, we analyze the existing models to discover the most suitable network and the most effective training strategy for our colon tumour segmentation case study.1.
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Juppet Q, De Martino F, Marcandalli E, Weigert M, Burri O, Unser M, Brisken C, Sage D. Deep Learning Enables Individual Xenograft Cell Classification in Histological Images by Analysis of Contextual Features. J Mammary Gland Biol Neoplasia 2021; 26:101-112. [PMID: 33999331 PMCID: PMC8236058 DOI: 10.1007/s10911-021-09485-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 04/05/2021] [Indexed: 02/06/2023] Open
Abstract
Patient-Derived Xenografts (PDXs) are the preclinical models which best recapitulate inter- and intra-patient complexity of human breast malignancies, and are also emerging as useful tools to study the normal breast epithelium. However, data analysis generated with such models is often confounded by the presence of host cells and can give rise to data misinterpretation. For instance, it is important to discriminate between xenografted and host cells in histological sections prior to performing immunostainings. We developed Single Cell Classifier (SCC), a data-driven deep learning-based computational tool that provides an innovative approach for automated cell species discrimination based on a multi-step process entailing nuclei segmentation and single cell classification. We show that human and murine cell contextual features, more than cell-intrinsic ones, can be exploited to discriminate between cell species in both normal and malignant tissues, yielding up to 96% classification accuracy. SCC will facilitate the interpretation of H&E- and DAPI-stained histological sections of xenografted human-in-mouse tissues and it is open to new in-house built models for further applications. SCC is released as an open-source plugin in ImageJ/Fiji available at the following link: https://github.com/Biomedical-Imaging-Group/SingleCellClassifier .
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Affiliation(s)
- Quentin Juppet
- Biomedical Imaging Group, School of Engineering, Ecole Polytechnique Fédéralé de Lausanne (EPFL), Lausanne, Switzerland
- EPFL Center for Imaging, Ecole Polytechnique Fédéralé de Lausanne (EPFL), Lausanne, Switzerland
- Swiss Institute for Experimental Cancer Research, School of Life Sciences, Ecole Polytechnique Fédéralé de Lausanne (EPFL), Lausanne, Switzerland
| | - Fabio De Martino
- Swiss Institute for Experimental Cancer Research, School of Life Sciences, Ecole Polytechnique Fédéralé de Lausanne (EPFL), Lausanne, Switzerland
| | - Elodie Marcandalli
- Swiss Institute for Experimental Cancer Research, School of Life Sciences, Ecole Polytechnique Fédéralé de Lausanne (EPFL), Lausanne, Switzerland
| | - Martin Weigert
- Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédéralé de Lausanne (EPFL), Lausanne, Switzerland
| | - Olivier Burri
- BioImaging & Optics Platform, Ecole Polytechnique Fédéralé de Lausanne (EPFL), Lausanne, Switzerland
| | - Michael Unser
- Biomedical Imaging Group, School of Engineering, Ecole Polytechnique Fédéralé de Lausanne (EPFL), Lausanne, Switzerland
| | - Cathrin Brisken
- Swiss Institute for Experimental Cancer Research, School of Life Sciences, Ecole Polytechnique Fédéralé de Lausanne (EPFL), Lausanne, Switzerland.
| | - Daniel Sage
- Biomedical Imaging Group, School of Engineering, Ecole Polytechnique Fédéralé de Lausanne (EPFL), Lausanne, Switzerland.
- EPFL Center for Imaging, Ecole Polytechnique Fédéralé de Lausanne (EPFL), Lausanne, Switzerland.
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Pathogen and Host-Pathogen Protein Interactions Provide a Key to Identify Novel Drug Targets. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11607-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
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Dimitriou N, Arandjelović O, Caie PD. Deep Learning for Whole Slide Image Analysis: An Overview. Front Med (Lausanne) 2019; 6:264. [PMID: 31824952 PMCID: PMC6882930 DOI: 10.3389/fmed.2019.00264] [Citation(s) in RCA: 119] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Accepted: 10/29/2019] [Indexed: 12/15/2022] Open
Abstract
The widespread adoption of whole slide imaging has increased the demand for effective and efficient gigapixel image analysis. Deep learning is at the forefront of computer vision, showcasing significant improvements over previous methodologies on visual understanding. However, whole slide images have billions of pixels and suffer from high morphological heterogeneity as well as from different types of artifacts. Collectively, these impede the conventional use of deep learning. For the clinical translation of deep learning solutions to become a reality, these challenges need to be addressed. In this paper, we review work on the interdisciplinary attempt of training deep neural networks using whole slide images, and highlight the different ideas underlying these methodologies.
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Affiliation(s)
- Neofytos Dimitriou
- School of Computer Science, University of St Andrews, St Andrews, United Kingdom
| | - Ognjen Arandjelović
- School of Computer Science, University of St Andrews, St Andrews, United Kingdom
| | - Peter D Caie
- School of Medicine, University of St Andrews, St Andrews, United Kingdom
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Harder N, Athelogou M, Hessel H, Brieu N, Yigitsoy M, Zimmermann J, Baatz M, Buchner A, Stief CG, Kirchner T, Binnig G, Schmidt G, Huss R. Tissue Phenomics for prognostic biomarker discovery in low- and intermediate-risk prostate cancer. Sci Rep 2018. [PMID: 29535336 PMCID: PMC5849604 DOI: 10.1038/s41598-018-22564-7] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
Tissue Phenomics is the discipline of mining tissue images to identify patterns that are related to clinical outcome providing potential prognostic and predictive value. This involves the discovery process from assay development, image analysis, and data mining to the final interpretation and validation of the findings. Importantly, this process is not linear but allows backward steps and optimization loops over multiple sub-processes. We provide a detailed description of the Tissue Phenomics methodology while exemplifying each step on the application of prostate cancer recurrence prediction. In particular, we automatically identified tissue-based biomarkers having significant prognostic value for low- and intermediate-risk prostate cancer patients (Gleason scores 6–7b) after radical prostatectomy. We found that promising phenes were related to CD8(+) and CD68(+) cells in the microenvironment of cancerous glands in combination with the local micro-vascularization. Recurrence prediction based on the selected phenes yielded accuracies up to 83% thereby clearly outperforming prediction based on the Gleason score. Moreover, we compared different machine learning algorithms to combine the most relevant phenes resulting in increased accuracies of 88% for tumor progression prediction. These findings will be of potential use for future prognostic tests for prostate cancer patients and provide a proof-of-principle of the Tissue Phenomics approach.
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Affiliation(s)
| | | | - Harald Hessel
- Institute for Pathology, Ludwig-Maximilians-University, Munich, Germany
| | | | - Mehmet Yigitsoy
- Definiens AG, Munich, Germany.,Carl Zeiss Meditec AG, Munich, Germany
| | | | | | - Alexander Buchner
- Department of Urology, Ludwig-Maximilians-University, Munich, Germany
| | - Christian G Stief
- Department of Urology, Ludwig-Maximilians-University, Munich, Germany
| | - Thomas Kirchner
- Institute for Pathology, Ludwig-Maximilians-University, Munich, Germany
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Use of systems biology to decipher host-pathogen interaction networks and predict biomarkers. Clin Microbiol Infect 2016; 22:600-6. [PMID: 27113568 DOI: 10.1016/j.cmi.2016.04.014] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2016] [Revised: 04/13/2016] [Accepted: 04/15/2016] [Indexed: 02/06/2023]
Abstract
In systems biology, researchers aim to understand complex biological systems as a whole, which is often achieved by mathematical modelling and the analyses of high-throughput data. In this review, we give an overview of medical applications of systems biology approaches with special focus on host-pathogen interactions. After introducing general ideas of systems biology, we focus on (1) the detection of putative biomarkers for improved diagnosis and support of therapeutic decisions, (2) network modelling for the identification of regulatory interactions between cellular molecules to reveal putative drug targets and (3) module discovery for the detection of phenotype-specific modules in molecular interaction networks. Biomarker detection applies supervised machine learning methods utilizing high-throughput data (e.g. single nucleotide polymorphism (SNP) detection, RNA-seq, proteomics) and clinical data. We demonstrate structural analysis of molecular networks, especially by identification of disease modules as a novel strategy, and discuss possible applications to host-pathogen interactions. Pioneering work was done to predict molecular host-pathogen interactions networks based on dual RNA-seq data. However, currently this network modelling is restricted to a small number of genes. With increasing number and quality of databases and data repositories, the prediction of large-scale networks will also be feasible that can used for multidimensional diagnosis and decision support for prevention and therapy of diseases. Finally, we outline further perspective issues such as support of personalized medicine with high-throughput data and generation of multiscale host-pathogen interaction models.
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Duffy DJ. Problems, challenges and promises: perspectives on precision medicine. Brief Bioinform 2015; 17:494-504. [DOI: 10.1093/bib/bbv060] [Citation(s) in RCA: 71] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2015] [Indexed: 12/11/2022] Open
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