1
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Oliveira GB, Pedrini H, Dias Z. TEMPROT: protein function annotation using transformers embeddings and homology search. BMC Bioinformatics 2023; 24:242. [PMID: 37291492 DOI: 10.1186/s12859-023-05375-0] [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: 12/20/2022] [Accepted: 06/02/2023] [Indexed: 06/10/2023] Open
Abstract
BACKGROUND Although the development of sequencing technologies has provided a large number of protein sequences, the analysis of functions that each one plays is still difficult due to the efforts of laboratorial methods, making necessary the usage of computational methods to decrease this gap. As the main source of information available about proteins is their sequences, approaches that can use this information, such as classification based on the patterns of the amino acids and the inference based on sequence similarity using alignment tools, are able to predict a large collection of proteins. The methods available in the literature that use this type of feature can achieve good results, however, they present restrictions of protein length as input to their models. In this work, we present a new method, called TEMPROT, based on the fine-tuning and extraction of embeddings from an available architecture pre-trained on protein sequences. We also describe TEMPROT+, an ensemble between TEMPROT and BLASTp, a local alignment tool that analyzes sequence similarity, which improves the results of our former approach. RESULTS The evaluation of our proposed classifiers with the literature approaches has been conducted on our dataset, which was derived from CAFA3 challenge database. Both TEMPROT and TEMPROT+ achieved competitive results on [Formula: see text], [Formula: see text], AuPRC and IAuPRC metrics on Biological Process (BP), Cellular Component (CC) and Molecular Function (MF) ontologies compared to state-of-the-art models, with the main results equal to 0.581, 0.692 and 0.662 of [Formula: see text] on BP, CC and MF, respectively. CONCLUSIONS The comparison with the literature showed that our model presented competitive results compared the state-of-the-art approaches considering the amino acid sequence pattern recognition and homology analysis. Our model also presented improvements related to the input size that the model can use to train compared to the literature methods.
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Affiliation(s)
| | - Helio Pedrini
- Institute of Computing, University of Campinas, Campinas, Brazil
| | - Zanoni Dias
- Institute of Computing, University of Campinas, Campinas, Brazil
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2
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da Silva ES, Pedrini H, Santos A. Applying Graph Neural Networks to Support Decision Making on Collective Intelligent Transportation Systems. IEEE Trans Netw Serv Manage 2023. [DOI: 10.1109/tnsm.2023.3257993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Affiliation(s)
| | - Helio Pedrini
- Institute of Computing, University of Campinas, Brazil
| | - Aldri Santos
- Computer Science Department, Federal University of Minas Gerais, Brazil
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Beauferris Y, Teuwen J, Karkalousos D, Moriakov N, Caan M, Yiasemis G, Rodrigues L, Lopes A, Pedrini H, Rittner L, Dannecker M, Studenyak V, Gröger F, Vyas D, Faghih-Roohi S, Kumar Jethi A, Chandra Raju J, Sivaprakasam M, Lasby M, Nogovitsyn N, Loos W, Frayne R, Souza R. Multi-Coil MRI Reconstruction Challenge-Assessing Brain MRI Reconstruction Models and Their Generalizability to Varying Coil Configurations. Front Neurosci 2022; 16:919186. [PMID: 35873808 PMCID: PMC9298878 DOI: 10.3389/fnins.2022.919186] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [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: 04/13/2022] [Accepted: 06/01/2022] [Indexed: 11/13/2022] Open
Abstract
Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods have the potential to accelerate the MRI acquisition process. Nevertheless, the scientific community lacks appropriate benchmarks to assess the MRI reconstruction quality of high-resolution brain images, and evaluate how these proposed algorithms will behave in the presence of small, but expected data distribution shifts. The multi-coil MRI (MC-MRI) reconstruction challenge provides a benchmark that aims at addressing these issues, using a large dataset of high-resolution, three-dimensional, T1-weighted MRI scans. The challenge has two primary goals: (1) to compare different MRI reconstruction models on this dataset and (2) to assess the generalizability of these models to data acquired with a different number of receiver coils. In this paper, we describe the challenge experimental design and summarize the results of a set of baseline and state-of-the-art brain MRI reconstruction models. We provide relevant comparative information on the current MRI reconstruction state-of-the-art and highlight the challenges of obtaining generalizable models that are required prior to broader clinical adoption. The MC-MRI benchmark data, evaluation code, and current challenge leaderboard are publicly available. They provide an objective performance assessment for future developments in the field of brain MRI reconstruction.
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Affiliation(s)
- Youssef Beauferris
- (AI)2 Lab, Electrical and Software Engineering, University of Calgary, Calgary, AB, Canada.,Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Jonas Teuwen
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, Netherlands.,Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, Netherlands.,Innovation Centre for Artificial Intelligence - Artificial Intelligence for Oncology, University of Amsterdam, Amsterdam, Netherlands
| | - Dimitrios Karkalousos
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, Netherlands
| | - Nikita Moriakov
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, Netherlands.,Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Matthan Caan
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, Netherlands
| | - George Yiasemis
- Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, Netherlands.,Innovation Centre for Artificial Intelligence - Artificial Intelligence for Oncology, University of Amsterdam, Amsterdam, Netherlands
| | - Lívia Rodrigues
- Medical Image Computing Lab, School of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil
| | - Alexandre Lopes
- Institute of Computing, University of Campinas, Campinas, Brazil
| | - Helio Pedrini
- Institute of Computing, University of Campinas, Campinas, Brazil
| | - Letícia Rittner
- Medical Image Computing Lab, School of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil
| | - Maik Dannecker
- Computer Aided Medical Procedures, Technical University of Munich, Munich, Germany
| | - Viktor Studenyak
- Computer Aided Medical Procedures, Technical University of Munich, Munich, Germany
| | - Fabian Gröger
- Computer Aided Medical Procedures, Technical University of Munich, Munich, Germany
| | - Devendra Vyas
- Computer Aided Medical Procedures, Technical University of Munich, Munich, Germany
| | | | - Amrit Kumar Jethi
- Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai, India
| | - Jaya Chandra Raju
- Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai, India
| | - Mohanasankar Sivaprakasam
- Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai, India.,Healthcare Technology Innovation Centre, Indian Institute of Technology Madras, Chennai, India
| | - Mike Lasby
- (AI)2 Lab, Electrical and Software Engineering, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Nikita Nogovitsyn
- Centre for Depression and Suicide Studies, St. Michael's Hospital, Toronto, ON, Canada.,Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
| | - Wallace Loos
- Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Radiology and Clinical Neurosciences, University of Calgary, Calgary, AB, Canada.,Seaman Family MR Research Centre, Foothills Medical Center, Calgary, AB, Canada
| | - Richard Frayne
- Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Radiology and Clinical Neurosciences, University of Calgary, Calgary, AB, Canada.,Seaman Family MR Research Centre, Foothills Medical Center, Calgary, AB, Canada
| | - Roberto Souza
- (AI)2 Lab, Electrical and Software Engineering, University of Calgary, Calgary, AB, Canada.,Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
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Córdova M, Pinto A, Hellevik CC, Alaliyat SAA, Hameed IA, Pedrini H, Torres RDS. Litter Detection with Deep Learning: A Comparative Study. Sensors (Basel) 2022; 22:s22020548. [PMID: 35062507 PMCID: PMC8812282 DOI: 10.3390/s22020548] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Revised: 12/30/2021] [Accepted: 01/05/2022] [Indexed: 11/28/2022]
Abstract
Pollution in the form of litter in the natural environment is one of the great challenges of our times. Automated litter detection can help assess waste occurrences in the environment. Different machine learning solutions have been explored to develop litter detection tools, thereby supporting research, citizen science, and volunteer clean-up initiatives. However, to the best of our knowledge, no work has investigated the performance of state-of-the-art deep learning object detection approaches in the context of litter detection. In particular, no studies have focused on the assessment of those methods aiming their use in devices with low processing capabilities, e.g., mobile phones, typically employed in citizen science activities. In this paper, we fill this literature gap. We performed a comparative study involving state-of-the-art CNN architectures (e.g., Faster RCNN, Mask-RCNN, EfficientDet, RetinaNet and YOLO-v5), two litter image datasets and a smartphone. We also introduce a new dataset for litter detection, named PlastOPol, composed of 2418 images and 5300 annotations. The experimental results demonstrate that object detectors based on the YOLO family are promising for the construction of litter detection solutions, with superior performance in terms of detection accuracy, processing time, and memory footprint.
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Affiliation(s)
- Manuel Córdova
- Institute of Computing, University of Campinas, Avenue Albert Einstein, Campinas 13083-852, Brazil; (M.C.); (H.P.)
| | - Allan Pinto
- Brazilian Center for Research in Energy and Materials (CNPEM), Brazilian Synchrotron Light Laboratory (LNLS), Campinas 13083-100, Brazil;
| | - Christina Carrozzo Hellevik
- Department of International Business, NTNU—Norwegian University of Science and Technology, Larsgårdsvegen 2, 6009 Alesund, Norway;
| | - Saleh Abdel-Afou Alaliyat
- Department of ICT and Natural Sciences, NTNU—Norwegian University of Science and Technology, Larsgårdsvegen 2, 6009 Alesund, Norway; (S.A.-A.A.); (I.A.H.)
| | - Ibrahim A. Hameed
- Department of ICT and Natural Sciences, NTNU—Norwegian University of Science and Technology, Larsgårdsvegen 2, 6009 Alesund, Norway; (S.A.-A.A.); (I.A.H.)
| | - Helio Pedrini
- Institute of Computing, University of Campinas, Avenue Albert Einstein, Campinas 13083-852, Brazil; (M.C.); (H.P.)
| | - Ricardo da S. Torres
- Department of ICT and Natural Sciences, NTNU—Norwegian University of Science and Technology, Larsgårdsvegen 2, 6009 Alesund, Norway; (S.A.-A.A.); (I.A.H.)
- Farm Technology Group and Wageningen Data Competence Center, Wageningen University and Research, 6708 PB Wageningen, The Netherlands
- Correspondence:
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de Oliveira GB, Pedrini H, Dias Z. Ensemble of Template-Free and Template-Based Classifiers for Protein Secondary Structure Prediction. Int J Mol Sci 2021; 22:11449. [PMID: 34768880 PMCID: PMC8583764 DOI: 10.3390/ijms222111449] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 10/18/2021] [Accepted: 10/20/2021] [Indexed: 11/16/2022] Open
Abstract
Protein secondary structures are important in many biological processes and applications. Due to advances in sequencing methods, there are many proteins sequenced, but fewer proteins with secondary structures defined by laboratory methods. With the development of computer technology, computational methods have (started to) become the most important methodologies for predicting secondary structures. We evaluated two different approaches to this problem-driven by the recent results obtained by computational methods in this task-(i) template-free classifiers, based on machine learning techniques; and (ii) template-based classifiers, based on searching tools. Both approaches are formed by different sub-classifiers-six for template-free and two for template-based, each with a specific view of the protein. Our results show that these ensembles improve the results of each approach individually.
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Amorim PHJ, Moraes TF, Silva JVL, Pedrini H, Ruben RB. Reconstruction of Panoramic Dental Images Through Bézier Function Optimization. Front Bioeng Biotechnol 2020; 8:794. [PMID: 32903678 PMCID: PMC7438751 DOI: 10.3389/fbioe.2020.00794] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [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: 03/25/2020] [Accepted: 06/22/2020] [Indexed: 11/13/2022] Open
Abstract
Computed tomography (CT) and X-ray images have been extensively used as a valuable diagnostic tool in dentistry for surgical planning and treatment. Nowadays, dental cone beam CT has been extensively used in dental clinics. Therefore, it is possible to employ three-dimensional (3D) data from the CT to reconstruct a two-dimensional (2D) panoramic dental image that provides a longitudinal view of the mandibular region of the patient, avoiding an additional exposure to X-ray. In this work, we developed a new automatic method for reconstructing 2D panoramic images of the dental arch based on 3D CT images, using Bézier curves and optimization techniques. The proposed method was applied to five patients, some of them with missing teeth, and smooth panoramic images with good contrast were obtained.
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Affiliation(s)
- Paulo H J Amorim
- Tridimensional Technology Division, Center for Information Technology Renato Archer, Campinas, Brazil
| | - Thiago F Moraes
- Tridimensional Technology Division, Center for Information Technology Renato Archer, Campinas, Brazil
| | - Jorge V L Silva
- Tridimensional Technology Division, Center for Information Technology Renato Archer, Campinas, Brazil
| | - Helio Pedrini
- Institute of Computing, University of Campinas, Campinas, Brazil
| | - Rui B Ruben
- CDRsp-ESTG, Polytechnic Institute of Leiria, Leiria, Portugal
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10
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Souza MRE, Maia HDA, Vieira MB, Pedrini H. Survey on visual rhythms: A spatio-temporal representation for video sequences. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.04.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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11
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Fazanaro DI, Pedrini H. A comparative analysis of Bayesian network structure learning algorithms applied to crime data. INTELL DATA ANAL 2020. [DOI: 10.3233/ida-194534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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12
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Affiliation(s)
- Julio Mendoza
- Institute of ComputingUniversity of Campinas Campinas‐SP Brazil
| | - Helio Pedrini
- Institute of ComputingUniversity of Campinas Campinas‐SP Brazil
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Abstract
Due to rapid advances in the development of surveillance cameras with high sampling rates, low cost, small size and high resolution, video-based action recognition systems have become more commonly used in various computer vision applications. Human operators can be supported with the aid of such systems to detect events of interest in video sequences, improving recognition results and reducing failure cases. In this work, we propose and evaluate a method to learn two-dimensional (2D) representations from video sequences based on an autoencoder framework. Spatial and temporal information is explored through a multi-stream convolutional neural network in the context of human action recognition. Experimental results on the challenging UCF101 and HMDB51 datasets demonstrate that our representation is capable of achieving competitive accuracy rates when compared to other approaches available in the literature.
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Affiliation(s)
| | - Helio Pedrini
- Institute of Computing, University of Campinas, Campinas-SP, 13083-852, Brazil
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14
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Moraes T, Amorim P, Da Silva JV, Pedrini H. Medical image interpolation based on 3D Lanczos filtering. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2019. [DOI: 10.1080/21681163.2019.1683469] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Thiago Moraes
- Division of 3D Technologies, Center for Information Technology Renato Archer, Campinas, Brazil
| | - Paulo Amorim
- Division of 3D Technologies, Center for Information Technology Renato Archer, Campinas, Brazil
| | - Jorge Vicente Da Silva
- Division of 3D Technologies, Center for Information Technology Renato Archer, Campinas, Brazil
| | - Helio Pedrini
- Institute of Computing, University of Campinas, Campinas, Brazil
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15
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Moraes TFD, Amorim PHJ, da Silva JVL, Pedrini H. Isosurface rendering of medical images improved by automatic texture mapping. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2018. [DOI: 10.1080/21681163.2016.1254069] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Thiago F. de Moraes
- Tridimensional Technology Division, Center for Information Technology Renato Archer, Campinas, Brazil
| | - Paulo H. J. Amorim
- Tridimensional Technology Division, Center for Information Technology Renato Archer, Campinas, Brazil
| | - Jorge V. L. da Silva
- Tridimensional Technology Division, Center for Information Technology Renato Archer, Campinas, Brazil
| | - Helio Pedrini
- Institute of Computing, University of Campinas, Campinas, Brazil
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16
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Amorim P, Moraes T, Fazanaro D, Silva J, Pedrini H. Shearlet and contourlet transforms for analysis of electrocardiogram signals. Comput Methods Programs Biomed 2018; 161:125-132. [PMID: 29852955 DOI: 10.1016/j.cmpb.2018.04.021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [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: 08/05/2017] [Revised: 04/18/2018] [Accepted: 04/26/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE Cardiac arrhythmia is an abnormal variation in the heart electrical activity that affects millions of people worldwide. Electrocardiogram (ECG) signals have been widely used to assess and diagnose cardiac abnormalities. METHODS A novel methodology based on shearlet and contourlet transforms for automatically classify an input ECG signal into different heart beat types is proposed and evaluated in this work. Classifiers are trained through a set of features extracted from these time-frequency coefficients. RESULTS Tests are conducted on MIT-BIH data set to demonstrate the effectiveness of the proposed classification method. The shearlet and contourlet transforms achieved high classification accuracy rates. CONCLUSIONS The developed system can help cardiologists obtain structural and functional information of the heart by means of ECG patterns, improving their diagnostic tasks.
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Affiliation(s)
- Paulo Amorim
- Tridimensional Technology Division, Center for Information Technology Renato Archer, Campinas, SP 13069-901, Brazil.
| | - Thiago Moraes
- Tridimensional Technology Division, Center for Information Technology Renato Archer, Campinas, SP 13069-901, Brazil.
| | - Dalton Fazanaro
- Institute of Computing, University of Campinas, Campinas, SP 13083-852, Brazil.
| | - Jorge Silva
- Tridimensional Technology Division, Center for Information Technology Renato Archer, Campinas, SP 13069-901, Brazil.
| | - Helio Pedrini
- Institute of Computing, University of Campinas, Campinas, SP 13083-852, Brazil.
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Cornejo JYR, Pedrini H, Machado-Lima A, Nunes FDLDS. Down syndrome detection based on facial features using a geometric descriptor. J Med Imaging (Bellingham) 2017; 4:044008. [PMID: 29250566 DOI: 10.1117/1.jmi.4.4.044008] [Citation(s) in RCA: 7] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Accepted: 11/20/2017] [Indexed: 11/14/2022] Open
Abstract
Down syndrome is one of the most common genetic disorders caused by chromosome abnormalities in humans. Among other physical characteristics, certain facial features are typically associated in people with Down syndrome. We investigate the problem of Down syndrome detection from a collection of face images. As the main contribution, a compact geometric descriptor is used to extract facial features from the images. Experiments are conducted on an available dataset to demonstrate the performance of the proposed methodology.
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Affiliation(s)
| | - Helio Pedrini
- University of Campinas, Institute of Computing, Campinas, Brazil
| | - Ariane Machado-Lima
- University of São Paulo, School of Arts, Science and Humanities, São Paulo, Brazil
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Silva ESAD, Pedrini H. Inferring patterns in mitochondrial DNA sequences through hypercube independent spanning trees. Comput Biol Med 2016; 70:51-57. [PMID: 26802544 DOI: 10.1016/j.compbiomed.2016.01.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [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: 10/08/2015] [Revised: 12/10/2015] [Accepted: 01/05/2016] [Indexed: 11/26/2022]
Abstract
Given a graph G, a set of spanning trees rooted at a vertex r of G is said vertex/edge independent if, for each vertex v of G, v≠r, the paths of r to v in any pair of trees are vertex/edge disjoint. Independent spanning trees (ISTs) provide a number of advantages in data broadcasting due to their fault tolerant properties. For this reason, some studies have addressed the issue by providing mechanisms for constructing independent spanning trees efficiently. In this work, we investigate how to construct independent spanning trees on hypercubes, which are generated based upon spanning binomial trees, and how to use them to predict mitochondrial DNA sequence parts through paths on the hypercube. The prediction works both for inferring mitochondrial DNA sequences comprised of six bases as well as infer anomalies that probably should not belong to the mitochondrial DNA standard.
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Affiliation(s)
| | - Helio Pedrini
- Institute of Computing, University of Campinas, Campinas-SP, 13083-852, Brazil
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Pinto A, Pedrini H, Schwartz WR, Rocha A. Face Spoofing Detection Through Visual Codebooks of Spectral Temporal Cubes. IEEE Trans Image Process 2015; 24:4726-4740. [PMID: 26276988 DOI: 10.1109/tip.2015.2466088] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Despite important recent advances, the vulnerability of biometric systems to spoofing attacks is still an open problem. Spoof attacks occur when impostor users present synthetic biometric samples of a valid user to the biometric system seeking to deceive it. Considering the case of face biometrics, a spoofing attack consists in presenting a fake sample (e.g., photograph, digital video, or even a 3D mask) to the acquisition sensor with the facial information of a valid user. In this paper, we introduce a low cost and software-based method for detecting spoofing attempts in face recognition systems. Our hypothesis is that during acquisition, there will be inevitable artifacts left behind in the recaptured biometric samples allowing us to create a discriminative signature of the video generated by the biometric sensor. To characterize these artifacts, we extract time-spectral feature descriptors from the video, which can be understood as a low-level feature descriptor that gathers temporal and spectral information across the biometric sample and use the visual codebook concept to find mid-level feature descriptors computed from the low-level ones. Such descriptors are more robust for detecting several kinds of attacks than the low-level ones. The experimental results show the effectiveness of the proposed method for detecting different types of attacks in a variety of scenarios and data sets, including photos, videos, and 3D masks.
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Cornejo JYR, Pedrini H, Flórez-Revuelta F. Facial Expression Recognition with Occlusions Based on Geometric Representation. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications 2015. [DOI: 10.1007/978-3-319-25751-8_32] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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Paiva JGS, Schwartz WR, Pedrini H, Minghim R. An Approach to Supporting Incremental Visual Data Classification. IEEE Trans Vis Comput Graph 2015; 21:4-17. [PMID: 26357017 DOI: 10.1109/tvcg.2014.2331979] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Automatic data classification is a computationally intensive task that presents variable precision and is considerably sensitive to the classifier configuration and to data representation, particularly for evolving data sets. Some of these issues can best be handled by methods that support users' control over the classification steps. In this paper, we propose a visual data classification methodology that supports users in tasks related to categorization such as training set selection; model creation, application and verification; and classifier tuning. The approach is then well suited for incremental classification, present in many applications with evolving data sets. Data set visualization is accomplished by means of point placement strategies, and we exemplify the method through multidimensional projections and Neighbor Joining trees. The same methodology can be employed by a user who wishes to create his or her own ground truth (or perspective) from a previously unlabeled data set. We validate the methodology through its application to categorization scenarios of image and text data sets, involving the creation, application, verification, and adjustment of classification models.
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Paiva JGS, Florian-Cruz L, Pedrini H, Telles GP, Minghim R. Improved similarity trees and their application to visual data classification. IEEE Trans Vis Comput Graph 2011; 17:2459-2468. [PMID: 22034367 DOI: 10.1109/tvcg.2011.212] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
An alternative form to multidimensional projections for the visual analysis of data represented in multidimensional spaces is the deployment of similarity trees, such as Neighbor Joining trees. They organize data objects on the visual plane emphasizing their levels of similarity with high capability of detecting and separating groups and subgroups of objects. Besides this similarity-based hierarchical data organization, some of their advantages include the ability to decrease point clutter; high precision; and a consistent view of the data set during focusing, offering a very intuitive way to view the general structure of the data set as well as to drill down to groups and subgroups of interest. Disadvantages of similarity trees based on neighbor joining strategies include their computational cost and the presence of virtual nodes that utilize too much of the visual space. This paper presents a highly improved version of the similarity tree technique. The improvements in the technique are given by two procedures. The first is a strategy that replaces virtual nodes by promoting real leaf nodes to their place, saving large portions of space in the display and maintaining the expressiveness and precision of the technique. The second improvement is an implementation that significantly accelerates the algorithm, impacting its use for larger data sets. We also illustrate the applicability of the technique in visual data mining, showing its advantages to support visual classification of data sets, with special attention to the case of image classification. We demonstrate the capabilities of the tree for analysis and iterative manipulation and employ those capabilities to support evolving to a satisfactory data organization and classification.
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Escanhoela CA, Cara ADM, Pedrini H, Azal Júnior W, Billis A. Multiple biopsies in bladder urothelial carcinomas. Correlation of atypical lesions with histological grade, clinical staging and number of tumors. Rev Paul Med 1992; 110:72-7. [PMID: 1340006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Twenty-eight cases of transitional cell carcinomas, (19 papillary cell carcinomas, 9 nonpapillary invasive carcinomas) with concomitant mucosal biopsies are reported. Multiple biopsies were obtained cystoscopically at diagnosis (during resection or biopsy of the main tumor) or afterwards, during post-operative evaluation. Fifteen patients (53.6%) were positive for dysplasia, carcinoma "in situ" or micro-invasive carcinoma in the biopsies. These lesions were correlated with the primary neoplasm in regard to: 1) histological grade. Atypical lesions were more frequent the higher the grade (0.01 < p < 0.05); 2) clinical staging. The possibility of finding atypical lesions was higher in cases with more advanced staging (0.01 < p < 0.05) and 3) presence of one or more tumors visible cystoscopically. The results were not statistically significant (0.10 < p < 0.50) but there was a trend toward a higher incidence of atypical lesions among patients with more than one tumor at cystoscopy. Performance of multiple mucosal biopsies is the only means of diagnosing for atypical lesions of the bladder because, due to their plane configuration, they are not detected cystoscopically. The presence of these lesions is very important because they influence the prognosis and the therapeutic measures.
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Affiliation(s)
- C A Escanhoela
- Departmento de Anatomia Patológica, Faculdade de Medicina, Universidade de Campinas (UNICAMP), Brasil
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de Faria JB, Alves MA, Pedrini H, Alves Filho G. [Nephrotic syndrome caused by membranous glomerulonephritis associated with chronic myeloid leukemia]. AMB Rev Assoc Med Bras 1991; 37:47-9. [PMID: 1658867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
A patient developed nephrotic syndrome four years after diagnosed chronic myelogenous leukemia. Renal histology showed characteristic changes of membranous glomerulopathy. To our knowledge, this is the first reported case of membranous glomerulopathy associated with chronic myelogenous leukemia.
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