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arXiv 2019年

作者： Marzahl, Christian Aubreville, Marc Bertram, Christof A. Gerlach, Stefan Maier, Jennifer Voigt, Jorn Hill, Jenny Klopfleisch, Robert Maier, Andreas Department of Computer Science Pattern Recognition Lab Friedrich-Alexander-Universität Research and Development Projects Euroimmun Medizinische Labordiagnostika Ag Institute of Veterinary Pathology Freie Universität Berlin Germany VetPath Laboratory Services AscotWA Australia

Modern, state-of-the-art deep learning approaches yield human like performance in numerous object detection and classification tasks. The foundation for their success is the availability of training datasets of substantially high quantity, which are expensive to create, especially in the field of medical imaging. Recently, crowdsourcing has been applied to create large datasets for a broad range of disciplines. This study aims to explore the challenges and opportunities of crowdalgorithm collaboration for the object detection task of grading cytology whole slide images. We compared the classical crowdsourcing performance of twenty participants with their results from crowd-algorithm collaboration. All participants performed both modes in random order on the same twenty images. Additionally, we introduced artificial systematic flaws into the precomputed annotations to estimate a bias towards accepting precomputed annotations. We gathered 9524 annotations on 800 images from twenty participants organised into four groups in concordance to their level of expertise with cytology. The crowd-algorithm mode improved on average the participants' classification accuracy by 7%, the mean average precision by 8% and the inter-observer Fleiss' kappa score by 20%, and reduced the time spent by 31%. However, two thirds of the artificially modified false labels were not recognised as such by the contributors. This study shows that crowd-algorithm collaboration is a promising new approach to generate large datasets when it is ensured that a carefully designed setup eliminates potential biases. Copyright © 2019, The Authors. All rights reserved.

关键词： Cytology

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A limitation of V-matrix based methods

arXiv

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arXiv 2018年

作者： Gauraha, Niharika Chaturvedi, Akshay Department of Pharmaceutical Biosciences Uppsala University Uppsala Sweden Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

To estimate the conditional probability functions based on the direct problem setting, V-matrix based method was proposed. We construct V-matrix based constrained quadratic programming problems for which the inequality constraints are inconsistent. In particular, we would like to present that the constrained quadratic optimization problem for conditional probability estimation using V-matrix method may not have a consistent solution always. Copyright © 2018, The Authors. All rights reserved.

关键词： Constrained optimization

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CASIA-SURF: A Large-scale Multi-modal Benchmark for Face Anti-spoofing

arXiv

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arXiv 2019年

作者： Zhang, Shifeng Liu, Ajian Wan, Jun Liang, Yanyan Guo, Guogong Escalera, Sergio Escalante, Hugo Jair Li, Stan Z. National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Macau University of Science and Technology Macau China Institute of Deep Learning Baidu Research and National Engineering Laboratory for Deep Learning Technology and Application Universitat de Barcelona Computer Vision Center Barcelona Catalonia Instituto Nacional de Astrofsica Ptica y Electrnica Puebla72840 Mexico

Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face anti-spoofing benchmark datasets in recent years. However, existing face antispoofing benchmarks have limited number of subjects (≤170) and modalities (≤2), which hinder the further development of the academic community. To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIASURF, which is the largest publicly available dataset for face antispoofing in terms of both subjects and modalities. Specifically, it consists of 1;000 subjects with 21;000 videos and each sample has 3 modalities (i.e., RGB, Depth and IR). We also provide comprehensive evaluation metrics, diverse evaluation protocols, training/validation/testing subsets and a measurement tool, developing a new benchmark for face anti-spoofing. Moreover, we present a novel multi-modal multi-scale fusion method as a strong baseline, which performs feature re-weighting to select the more informative channel features while suppressing the less useful ones for each modality across different scales. Extensive experiments have been conducted on the proposed dataset to verify its significance and generalization capability. The dataset is available at http://***/***/chalearnfacespoofingattackdete/. Copyright © 2019, The Authors. All rights reserved.

关键词： Face recognition

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Reward Processing in Novelty Seekers: A Transdiagnostic Psychiatric Imaging Biomarker

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BIOLOGICAL PSYCHIATRY 2021年第8期90卷 529-539页

作者： Qi, Shile Schumann, Gunter Bustillo, Juan Turner, Jessica A. Jiang, Rongtao Zhi, Dongmei Fu, Zening Mayer, Andrew R. Vergara, Victor M. Silva, Rogers F. Iraji, Armin Chen, Jiayu Damaraju, Eswar Ma, Xiaohong Yang, Xiao Stevens, Michael Mathalon, Daniel H. Ford, Judith M. Voyvodic, James Mueller, Bryon A. Belger, Aysenil Potkin, Steven G. Preda, Adrian Zhuo, Chuanjun Xu, Yong Chu, Congying Banaschewski, Tobias Barker, Gareth J. Bokde, Arun L. W. Quinlan, Erin Burke Desrivieres, Sylvane Flor, Herta Grigis, Antoine Garavan, Hugh Gowland, Penny Heinz, Andreas Martinot, Jean-Luc Martinot, Marie-Laure Paillere Artiges, Eric Nees, Frauke Orfanos, Dimitri Papadopoulos Paus, Tomas Poustka, Luise Hohmann, Sarah Frohner, Juliane H. Smolka, Michael N. Walter, Henrik Whelan, Robert Calhoun, Vince D. Sui, Jing [a]Tri-institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute Technology and Emory University Atlanta Georgia [b]Department of Psychiatry University of New Mexico Albuquerque New Mexico [c]Department of Psychology Georgia State University Atlanta Georgia [d]Olin Neuropsychiatry Research Center Hartford Connecticut [e]Department of Psychiatry University of California San Francisco San Francisco California [f]Department of Radiology Duke University Durham North Carolina [g]Department of Psychiatry University of North Carolina at Chapel Hill Chapel Hill North Carolina [h]Department of Psychiatry University of Minnesota Minneapolis Minnesota [i]Department of Psychiatry University of California Irvine Irvine California [j]Departments of Psychiatry and Psychology University of Vermont Burlington Vermont [k]Centre for Population Neuroscience and Stratified Medicine Institute for Science and Technology of Brain-Inspired Intelligence Fudan University Shanghai China [l]University of Chinese Academy of Sciences Beijing China [dd]Department of Computer Science and Engineering Nanjing University of Aeronautics and Astronautics Nanjing China [m]Brainnetome Center and National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China [ee]State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing China [n]Psychiatric Laboratory and Mental Health Center the State Key Laboratory of Biotherapy West China Hospital of Sichuan University Chengdu China [o]Department of Psychiatric-Neuroimaging-Genetics and Morbidity Laboratory Nankai University Affiliated Anding Hospital Tianjin China [p]Department of Humanities and Social Science Shanxi Medical University Taiyuan China [q]Department of Psychiatry and Psychotherapy Charité – Universitätsmedizin Berlin Campus Charité Mitte corporate member of Freie Universität Berlin Humboldt-Univers

BACKGROUND: Dysfunctional reward processing is implicated in multiple mental disorders. Novelty seeking (NS) assesses preference for seeking novel experiences, which is linked to sensitivity to reward environmental cues. METHODS: A subset of 14-year-old adolescents (IMAGEN) with the top 20% ranked high-NS scores was used to identify high-NS-associated multimodal components by supervised fusion. These features were then used to longitudinally predict five different risk scales for the same and unseen subjects (an independent dataset of subjects at 19 years of age that was not used in predictive modeling training at 14 years of age) (within IMAGEN, n z 1100) and even for the corresponding symptom scores of five types of patient cohorts (non-IMAGEN), including drinking (n = 313), smoking (n = 104), attention-deficit/hyperactivity disorder (n = 320), major depressive disorder (n = 81), and schizophrenia (n = 147), as well as to classify different patient groups with diagnostic labels. RESULTS: Multimodal biomarkers, including the prefrontal cortex, striatum, amygdala, and hippocampus, associated with high NS in 14-year-old adolescents were identified. The prediction models built on these features are able to longitudinally predict five different risk scales, including alcohol drinking, smoking, hyperactivity, depression, and psychosis for the same and unseen 19-year-old adolescents and even predict the corresponding symptom scores of five types of patient cohorts. Furthermore, the identified reward-related multimodal features can classify among attention-deficit/hyperactivity disorder, major depressive disorder, and schizophrenia with an accuracy of 87.2%. CONCLUSIONS: Adolescents with higher NS scores can be used to reveal brain alterations in the reward-related system, implicating potential higher risk for subsequent development of multiple disorders. The identified high-NS- associated multimodal reward-related signatures may serve as a transdiagnostic neuroimaging bi

关键词： ADHD Attention-deficit/hyperactivity disorder Major depressive disorders MDD Novelty seeking Reward processing Schizophrenia Substance use

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CRNN Based Jersey-Bib Number/Text recognition in Sports and Marathon Images

CRNN Based Jersey-Bib Number/Text Recognition in Sports and ...

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International Conference on Document Analysis and recognition

作者： Sauradip Nag Raghavendra Ramachandra Palaiahnakote Shivakumara Umapada Pal Tong Lu Mohan Kankanhalli Department of Computer Science & Engineering Kalyani Government Engineering College Kalyani India Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Norway Faculty of Computer System and Information Technology University of Malaya Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University China Department of computer science National University of Singapore Singapore

The primary challenge in tracing the participants in sports and marathon video or images is to detect and localize the jersey/Bib number that may present in different regions of their outfit captured in cluttered environment conditions. In this work, we proposed a new framework based on detecting the human body parts such that both Jersey Bib number and text is localized reliably. To achieve this, the proposed method first detects and localize the human in a given image using Single Shot Multibox Detector (SSD). In the next step, different human body parts namely, Torso, Left Thigh, Right Thigh, that generally contain a Bib number or text region is automatically extracted. These detected individual parts are processed individually to detect the Jersey Bib number/text using a deep CNN network based on the 2-channel architecture based on the novel adaptive weighting loss function. Finally, the detected text is cropped out and fed to a CNN-RNN based deep model abbreviated as CRNN for recognizing jersey/Bib/text. Extensive experiments are carried out on the four different datasets including both bench-marking dataset and a new dataset. The performance of the proposed method is compared with the state-of-the-art methods on all four datasets that indicates the improved performance of the proposed method on all four datasets.

关键词： Text recognition Sports Image recognition Hip Thigh Torso Biological system modeling

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Triplet Graph Convolutional Network for Multi-scale Analysis of Functional Connectivity Using Functional MRI 1

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1st International Workshop on Graph Learning in Medical Imaging, GLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2019

作者： Yao, Dongren Liu, Mingxia Wang, Mingliang Lian, Chunfeng Wei, Jie Sun, Li Sui, Jing Shen, Dinggang Brainnetome Center & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Department of Radiology and BRIC University of North Carolina at Chapel Hill Chapel HillNC27599 United States College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Computer Science Northwestern Polytechnical University Xi’an710072 China National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health Ministry of Health Peking University Beijing100191 China

ISBN: (数字)9783030358174

ISBN: (纸本)9783030358167

Brain functional connectivity (FC) derived from resting-state functional MRI (rs-fMRI) data has become a powerful approach to measure and map brain activity. Using fMRI data, graph convolutional network (GCN) has recently shown its superiority in learning discriminative representations of brain FC networks. However, existing studies typically utilize one specific template to partition the brain into multiple regions-of-interest (ROIs) for constructing FCs, which may limit the analysis to a single spatial scale (i.e., a fixed graph) determined by the template. Also, previous methods usually ignore the underlying high-order (e.g., triplet) association among subjects. To this end, we propose a multi-scale triplet graph convolutional network (MTGCN) for brain functional connectivity analysis with rs-fMRI data. Specifically, we first employ multi-scale templates for coarse-to-fine ROI parcellation to construct multi-scale FCs for each subject. We then develop a triplet GCN (TGCN) model to learn multi-scale graph representations of brain FC networks, followed by a weighted fusion scheme for classification. Experimental results on 1,218 subjects suggest the efficacy or our method. © 2019, Springer Nature Switzerland AG.

关键词： Brain

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Preface

Advances in Intelligent Systems and Computing

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Advances in Intelligent Systems and Computing 2020年 1022 AISC卷 v-vi页

作者： Chaudhuri, Bidyut B. Nakagawa, Masaki Khanna, Pritee Kumar, Sanjeev Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Techno India University Kolkata India Department of Advanced Information Technology and Computer Sciences Tokyo Institute of Agriculture and Technology Koganei Tokyo Japan Department of Computer Science Indian Institute of Information Technology Design and Manufacturing Jabalpur Jabalpur Madhya Pradesh India Department of Mathematics Indian Institute of Technology Roorkee Roorkee Uttarakhand India

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ICDAR2019 Robust Reading Challenge on Multi-lingual Scene Text Detection and recognition — RRC-MLT-2019

ICDAR2019 Robust Reading Challenge on Multi-lingual Scene Te...

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International Conference on Document Analysis and recognition

作者： Nibal Nayef Yash Patel Michal Busta Pinaki Nath Chowdhury Dimosthenis Karatzas Wafa Khlif Jiri Matas Umapada Pal Jean-Christophe Burie Cheng-lin Liu Jean-Marc Ogier no affiliation The Robotics Institute Carnegie Mellon Universiry Pittsburgh USA Department of Cybernetics Czech Technical University Prague Czech Republic CVPR unit Indian Statistical Institute India Computer Vision Center Universitat Autònoma de Barcelona Spain L3i Laboratory University of La Rochelle France National Laboratory of Pattern Recognition Institute of Automation of Chinese Academy of Sciences China

With the growing cosmopolitan culture of modern cities, the need of robust Multi-Lingual scene Text (MLT) detection and recognition systems has never been more immense. With the goal to systematically benchmark and push the state-of-the-art forward, the proposed competition builds on top of the RRC-MLT-2017 with an additional end-to-end task, an additional language in the real images dataset, a large scale multi-lingual synthetic dataset to assist the training, and a baseline End-to-End recognition method. The real dataset consists of 20,000 images containing text from 10 languages. The challenge has 4 tasks covering various aspects of multi-lingual scene text: (a) text detection, (b) cropped word script classification, (c) joint text detection and script classification and (d) end-to-end detection and recognition. In total, the competition received 60 submissions from the research and industrial communities. This paper presents the dataset, the tasks and the findings of the presented RRC-MLT-2019 challenge.

关键词： Task analysis Text recognition Training Benchmark testing Protocols Rendering (computer graphics)

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ICDAR2019 Robust reading challenge on multi-lingual scene text detection and recognition – RRC-MLT-2019

arXiv

引用

arXiv 2019年

作者： Nayef, Nibal Patel, Yash Busta, Michal Chowdhury, Pinaki Nath Karatzas, Dimosthenis Khlif, Wafa Matas, Jiri Pal, Umapada Burie, Jean-Christophe Liu, Cheng-lin Ogier, Jean-Marc L3i Laboratory University of La Rochelle France Computer Vision Center Universitat Autònoma de Barcelona Spain CVPR unit Indian Statistical Institute India Robotics Institute Carnegie Mellon Universiry Pittsburgh United States Center for Machine Perception Department of Cybernetics Czech Technical University Prague Czech Republic National Laboratory of Pattern Recognition Institute of Automation of Chinese Academy of Sciences China

关键词： Large dataset

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A joint probabilistic model for speckle variance, amplitude decorrelation and interframe variance (IFV) optical coherence tomography angiography

A joint probabilistic model for speckle variance, amplitude ...

引用

Workshop on Bildverarbeitung fur die Medizin, 2018

作者： Ploner, Stefan B. Riess, Christian Schottenhamml, Julia Moult, Eric M. Waheed, Nadia K. Fujimoto, James G. Maier, Andreas Pattern Recognition Lab FAU Erlangen-Nürnberg Erlangen Germany Department of Electrical Engineering and Computer Science Research Laboratory for Electronics Massachusetts Institute of Technology Cambridge United States Ophthalmology New England Eye Center Tufts Medical Center Boston United States

ISBN: (纸本)9783662580950

Optical Coherence Tomography Angiography (OCTA) is a general method to visualize blood flow in biological tissue. Despite its good results in practice, the commonly used Amplitude Decorrelation OCTA (AD-OCTA) measure suffers from a well-understood objective function, which makes it challenging to mathematically model post processing tasks like, e.g., denoising. In this paper, a probabilistic model is developed for the three OCTA measures Speckle Variance OCTA, ADOCTA and the newly proposed Interframe Variance OCTA (IFV-OCTA) to enable further tasks like regularization-based denoising. From a theoretical point of view, IFV-OCTA is shown to be in-between the other two methods and can act as a link between them. A small sized observer study suggests that the image quality of IFV-OCTA is comparable to the other methods. IFV-OCTA is a promising OCTA measure for algorithms that require a dependency on the interscan time. © Springer-Verlag GmbH Deutschland 2018.

关键词： Angiography

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