medical image analysis elsevier

The poor explainability leads to distrust from clinicians who are trained to make explainable clinical inferences. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We are always looking for ways to improve customer experience on Elsevier.com. Xin Yi, Ekta Walia, Paul Babyn December 2019Volume 58, Jo Schlemper, Ozan Oktay and 5 moreOpen AccessApril 2019Volume 53, Pages 197-207, Thomas Schlegl, Philipp Seebck and 3 moreMay 2019Volume 54, Pages 30-44, Shervin Minaee, Rahele Kafieh and 3 moreOpen AccessOctober 2020Volume 65, Veronika Cheplygina, Marleen de Bruijne, Josien P.W. He has served in the Board of Directors, The Medical Image Computing and Computer Assisted Intervention (MICCAI) Society, in 2012-2015. LMISA: A lightweight multi-modality image segmentation network via domain adaptation using gradient magnitude and shape constraint. The National Climate Change Adaptation Strategy 2035 also . The Article Publishing Charge for this journal is USD3970, excluding taxes. Deep learning models are essentially black boxes that do not offer explainability of their decision-making process which in turn makes it hard to debug them when necessary. Pluim May 2019Volume 54, Pages 280-296, Bob D. de Vos, Floris F. Berendsen and 4 moreFebruary 2019Volume 52, Pages 128-143, Nima Tajbakhsh, Laura Jeyaseelan and 4 moreJuly 2020Volume 63, Simon Graham, Quoc Dang Vu and 5 moreDecember 2019Volume 58, Guilherme Aresta, Teresa Arajo and 34 moreOpen AccessAugust 2019Volume 56, Pages 122-139, Jos Ignacio Orlando, Huazhu Fu and 29 moreJanuary 2020Volume 59, Mahendra Khened, Varghese Alex Kollerathu, Ganapathy Krishnamurthi January 2019Volume 51, Pages 21-45, Jianpeng Zhang, Yutong Xie, Qi Wu, Yong Xia May 2019Volume 54, Pages 10-19, Junhao Wen, Elina Thibeau-Sutre and 8 moreOpen AccessJuly 2020Volume 63, L. 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Sabuncu October 2019Volume 57, Pages 226-236, Christian Payer, Darko tern, Horst Bischof, Martin Urschler Open AccessMay 2019Volume 54, Pages 207-219, Copyright 2022 Elsevier, except certain content provided by third parties, Cookies are used by this site. The recognition of the association between climate and health continues to increase in academic and political communities. Your publication choice will have no effect on the peer review process or acceptance of your submission. Pluim, Bob D. de Vos, Floris F. Berendsen and 4 more, Nima Tajbakhsh, Laura Jeyaseelan and 4 more, Guilherme Aresta, Teresa Arajo and 34 more, Jos Ignacio Orlando, Huazhu Fu and 29 more, Mahendra Khened, Varghese Alex Kollerathu, Ganapathy Krishnamurthi, Jianpeng Zhang, Yutong Xie, Qi Wu, Yong Xia, Junhao Wen, Elina Thibeau-Sutre and 8 more, Felix Ambellan, Alexander Tack, Moritz Ehlke, Stefan Zachow, Davood Karimi, Haoran Dou, Simon K. Warfield, Ali Gholipour, Chetan L. Srinidhi, Ozan Ciga, Anne L. Martel, Jingfan Fan, Xiaohuan Cao, Pew Thian Yap, Dinggang Shen, Tanya Nair, Doina Precup, Douglas L. Arnold, Tal Arbel, Ida Hggstrm, C. Ross Schmidtlein, Gabriele Campanella, Thomas J. Fuchs, Adrian V. Dalca, Guha Balakrishnan, John Guttag, Mert R. Sabuncu, Christian Payer, Darko tern, Horst Bischof, Martin Urschler. Copyright 2022 Elsevier, except certain content provided by third parties, Cookies are used by this site. If you wish to place a tax exempt order please contact us. Privacy Policy Guotai Wang, PhD. Sign in to view your account details and order history, Explainable and Generalizable Deep Learning Methods for Medical Image Computing. Flexible - Read on multiple operating systems and devices. Image Representation Schemes with Classical (Non-Deep) Features, 13.3. The published journal article cannot be shared publicly, for example on ResearchGate or Academia.edu, to ensure the sustainability of peer-reviewed research in journal publications. Easy - Download and start reading immediately. Medical Image Analysis provides a forum for the dissemination of new research results in the field of medical and biological image analysis, with special emphasis on efforts related to the applications of computer vision, virtual reality and robotics to biomedical imaging problems. It is published by Elsevier. She was a visiting Professor at the Radiology Dept. Head, Medical Image Processing and Analysis Lab, Biomedical Engineering Department, Faculty of Engineering, Tel-Aviv University, Israel. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using a wide variety of application areas. Improve your chances of getting published in Medical Image Analysis with Researcher.Life. Medical Image Analysis Editorial Board Ruogu Fang ISSN: 1361-8415 Medical Image Analysis Submit your Paper View Articles Guide for authors Track your paper Order journal Ruogu Fang Regular Members University of Florida, Gainesville, Florida, United States of America Scalable High Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning, 12. Faculty of Engineering, Tel-Aviv University. Deep Learning Tissue Segmentation in Cardiac Histopathology Images, 9. An Introduction to Neural Networks and Deep Learning, 2. Cookie Settings, Terms and Conditions Principal Key Expert, Medical Image Analysis, Siemens Healthcare Technology Center, Princeton, New Jersey, USA. Notably, climate-related content was explicitly included in the annual work priorities in the Healthy China Action report in 2022, echoing the policy recommendations given in the 2021 China Lancet Countdown report. Elsevier; 2008. pp. Sometimes an interesting issues related to using image. offered the lowest possible Article Publishing Charge, Learn more about Elsevier's pricing policy, Benefits of publishing open access with Elsevier, Journal Article Publishing Support Center. Convolutional Neural Network Architecture, 13.2. Similar to their micron-scale counterparts, microbubbles (1-10 m), they can act as ultrasound contrast agents as well as locally enhance therapeutic uptake. Elsevier Medical Image Analysis Medical Image Analysis provides a forum for the dissemination of new research results in the field of medical and biological image analysis, with special emphasis on efforts related to the applications of computer vision, virtual reality and robotics to biomedical imaging prob.. Read More Medicine I highly recommend: Medical Image Analysis (Elsevier), BMC Medical Imaging (BioMed Central Ltd) and Journal of Medical Imaging (SPIE). Articles are freely available to both subscribers and the wider public with permitted reuse. He serves as an editorial board member for six international journals. Deep learning has recently revolutionized the methods used for medical image computing due to automated feature discovery and superior results. Medical Image Analysis offers authors two choices to publish their research: In accordance with funding body requirements, Elsevier does offer alternative open access publishing options. Extending the Representation Using Feature Fusion and Selection, 16.2. Computer-based image analysis systems enable automated and efficient search of similar cases in large-scale databases. The University of North Carolina at Charlotte, and SenseTime Research. Description Medical Image Analysis provides a forum for the dissemination of new research results in the field of medical and biological image analysis, with special emphasis on efforts related to the applications of computer vision, virtual reality and robotics to biomedical imaging problems. Hayit Greenspan is a Tenured Professor at the Biomedical Engineering Dept. She has received several awards and is a coauthor on several patents. Elsevier partners with funding bodies to provide guidance for authors on how to comply with funding body open access policies. The computer processing and analysis of medical images involve image retrieval, image creation, image analysis, and image-based visualization [ 2 ]. The generalizability problem becomes even more conspicuous when a deep learning model trained on data from a given medical center is deployed to other medical centers whose data have significant variations or there is a domain shift from the training set. Deep learning is providing exciting solutions for medical image analysis problems and is seen as a key method for future applications. Medical Image Analysis offers authors two choices to publish their research: In accordance with funding body requirements, Elsevier does offer alternative open access publishing options. Dr. Greenspan has over 150 publications in leading international journals and conference proceedings. Abstract "The Handbook of Medical Image Processing and Analysis is a comprehensive compilation of concepts and techniques used for processing and analyzing medical images after they have been generated or digitized. (University College London) The Medical Image Analysis /MICCAI Best Paper award is presented annually and recognizes the top extended conference paper from the previous year's International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). Easily read eBooks on smart phones, computers, or any eBook readers, including Kindle. Key Features Readership Table of Contents Product details Academic and industry researchers and graduate students in medical imaging, computer vision, biomedical engineering. We are always looking for ways to improve customer experience on Elsevier.com. Mina Jafari, Susan Francis, Jonathan M. Garibaldi, Xin Chen. Cookie Notice We offer authors a choice of user licenses, which define the permitted reuse of articles. ACE=angiotensin converting enzyme. Methods that offer explainability and interpretability in deep learning models for disease characterization and classification using medical images Learning interpretable knowledge from unannotated/annotated medical images Explainable deep learning networks for computer-aided diagnosis from medical images The journal View full aims & scope Insights $3970* strings of text saved by a browser on the user's device. This is a Transformative Journal. Teaches how algorithms are applied to a broad range of application areas, including Chest X-ray, breast CAD, lung and chest, microscopy and pathology, etc. Full optimal doses for each treatment are given in the appendix (p 5). We would like to ask you for a moment of your time to fill in a short questionnaire, at the end of your visit. About. 83-113. book section 4. Xiaolei Huang, Pennsylvania State University. 1. Sign in to view your account details and order history. He is an editorial board member for Medical Image Analysis journal and a fellow of American Institute of Medical and Biological Engineering (AIMBE). In these systems, . Extracting Deep Features from a Pre-Trained CNN Model, 13.4. Articles are made available to subscribers as well as developing countries and patient groups through our. Paper submission deadline: December 17, 2021. Fundamentals of Natural Language Processing, 17.5. Cookie Settings, Terms and Conditions Alongside The International Journal of Computer Assisted Radiology and Surgery, Medical Image Analysis is an official publication of The Medical Image Computing and Computer Assisted Interventions Society [2] and is published by Elsevier . He was a tenure-track assistant professor in the University of Pennsylvanian (UPenn), and a faculty member in the Johns Hopkins University. In addition, their generalizability is still limited in clinical environments due to the many different imaging protocols, large variations in image-based manifestation of pathologies and rare diseases whose related data may have not been used during training. Stanford University, and is currently affiliated with the International Computer Science Institute (ICSI) at Berkeley. We would like to ask you for a moment of your time to fill in a short questionnaire, at the end of your visit. Experimental Design and Implementation, 10.3. He is currently directing the Center for Image Informatics and Analysis, the Image Display, Enhancement, and Analysis (IDEA) Lab in the Department of Radiology, and also the medical image analysis core in the BRIC. Sitemap. Immediately download your eBook while waiting for print delivery. Shaoting Zhang, PhD. Sitemap. An author can also self-archive their author manuscript immediately and enable public access from their institution's repository after an embargo period. S. Kevin Zhou, Ph.D. is currently a Principal Key Expert Scientist at Siemens Healthcare Technology Center, leading a team of full time research scientists and students dedicated to researching and developing innovative solutions for medical and industrial imaging products. When submitting your manuscript please select the article type VSI: Expl.&Gener. Download : Download high-res image (662KB) Download : Download full-size image; Figure 2. Open - Buy once, receive and download all available eBook formats, including PDF, EPUB, and Mobi (for Kindle). Sign in to view your account details and order history. This is the version that has been accepted for publication and which typically includes author-incorporated changes suggested during submission, peer review and in editor-author communications. ), or their login data. I have 21++ years of experience in: Creating new . Cookie Settings, Terms and ConditionsPrivacy PolicyCookie NoticeSitemap, Veronika A. Zimmer, Alberto Gomez and 11 more, Jasper Linmans, Stefan Elfwing, Jeroen van der Laak, Geert Litjens, Juana Gonzlez-Bueno Puyal, Patrick Brandao and 7 more, Tao Wei, Angelica I. Aviles-Rivero and 5 more, Filip Rusak, Rodrigo Santa Cruz and 7 more, Mojtaba Lashgari, Nishant Ravikumar and 5 more, Francesco Masia, Walter Dewitte, Paola Borri, Wolfgang Langbein, David Schuhmacher, Stephanie Schrner and 10 more, Ivona Najdenkoska, Xiantong Zhen, Marcel Worring, Ling Shao, Arezoo Zakeri, Alireza Hokmabadi and 7 more, Juan Carlos ngeles Cern, Gilberto Ochoa Ruiz, Leonardo Chang, Sharib Ali, Xiebo Geng, Xiuli Liu, Shenghua Cheng, Shaoqun Zeng. I am Expert Scientist in Computational Imaging, with a passion for the co-design and joint-optimization of illumination, optical lens, image sensors, signal processing and artificial intelligence techniques to go beyond human vision or capturing/revealing the invisible. Tom Vercauteren, PhD. Describes deep learning methods and the theories behind approaches for medical image analysis Teaches how algorithms are applied to a broad range of application areas, including Chest X-ray, breast CAD, lung and chest, microscopy and pathology, etc. Automatic Interpretation of Carotid IntimaMedia Thickness Videos Using Convolutional Neural Networks, 6. Consequently, there is an urgent need for innovative methodologies to improve the explainability and generalizability of deep learning methods that will enable them to be used routinely in clinical practice. Deep Learning for Medical Image Analysis is a great learning resource for academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Dr. Greenspans research focuses on image modeling and analysis, deep learning, and content-based image retrieval. Deep Voting and Structured Regression for Microscopy Image Analysis, 8. Dinggang Shen is a Professor of Radiology, Biomedical Research Imaging Center (BRIC), Computer Science, and Biomedical Engineering in the University of North Carolina at Chapel Hill (UNC-CH). Copyright 2022 Elsevier, except certain content provided by third parties, Cookies are used by this site. Unsupervised Synthesis Using Mutual Information Maximization, 17.2. Cookie Settings, Terms and ConditionsPrivacy PolicyCookie NoticeSitemap, Special Issue on Explainable and Generalizable Deep Learning Methods for Medical Image Computing, Explainable/interpretable deep learning models for medical image computing, Methods that offer explainability and interpretability in deep learning models for disease characterization and classification using medical images, Learning interpretable knowledge from unannotated/annotated medical images, Explainable deep learning networks for computer-aided diagnosis from medical images, Incorporation of clinical knowledge into deep learning models for interpretable medical image analytics methods, Generalizable deep learning methods when the training medical image datasets are small, Novel data augmentation, regularization and training strategies to reduce over-fitting, especially in case of rare diseases and high-dimensional images where the training set is small, Integration of prior medical knowledge into deep learning models for medical image analysis, Human interaction to improve the robustness when dealing with rare or complex cases, such as for segmentation, Generalizable deep learning methods in cases of images with potential domain shift, Learning domain-invariant features for images from different modalities, scanning protocols and patient groups, Unsupervised, weakly supervised and semi-supervised model adaptation to new domains for medical image computing, Out-of-distribution detection methods when applying a model to novel data not previously trained on, Generalizable models for images from multi-centers, multi-modalities, multi-diseases or multi-organs. Recently, it has been shown that the reduced size of NBs (<1 m) promotes increased uptake and accumulation in tumor interstitial space . He has published more than 700 papers in the international journals and conference proceedings. propos. Rutgers University. Includes a Foreword written by Nicholas Ayache, 6.3. Currently her Lab is funded for Deep Learning in Medical Imaging by the INTEL Collaborative Research Institute for Computational Intelligence (ICRI-CI). The overall rank of Medical Image Analysis is 364 . Convolutional Neural Networks for Robust and Real-Time 2-D/3-D Registration, Part V: Computer-Aided Diagnosis and Disease Quantification, 13. According to SCImago Journal Rank (SJR), this journal is ranked 4.172. Most Cited Articles - Medical Image Analysis - Journal - Elsevier Medical Image Analysis provides a forum for the dissemination of new research results in the field of medical and biological image analysis, with special emph He has published over 150 book chapters and peer-reviewed journal and conference papers, registered over 250 patents and inventions, written two research monographs, and edited three books. Mitosis Detection from Histology Images, 6.4. This journal has an embargo period of 24 months. All articles published gold open access will be immediately and permanently free for everyone to read and download. Learn more about Elsevier's pricing policy. Lipid-shelled nanobubbles (NBs) are emerging as potential dual diagnostic and therapeutic agents. Predicting Presence or Absence of Frequent Disease Types. Song Y, et al. Dr. Greenspan is a member of several journal and conference program committees, including SPIE medical imaging, IEEE_ISBI and MICCAI. Cookie Settings, Terms and ConditionsPrivacy PolicyCookie NoticeSitemap, Thomas Schlegl, Philipp Seebck and 3 more, Veronika Cheplygina, Marleen de Bruijne, Josien P.W. Privacy Policy Sign in to view your account details and order history, Veronika A. Zimmer, Alberto Gomez and 11 moreOpen Access, Andrew Moyes, Richard Gault and 4 moreOpen Access, Jasper Linmans, Stefan Elfwing, Jeroen van der Laak, Geert Litjens Open Access, Fabian Laumer, Mounir Amrani and 6 moreOpen Access, Tianfei Zhou, Liulei Li and 4 moreOpen Access, Raluca Jalaboi, Frederik Faye and 4 moreOpen Access, Reuben Dorent, Aaron Kujawa and 38 moreOpen Access, Changyeop Shin, Hyun Ryu and 5 moreOpen Access, Juana Gonzlez-Bueno Puyal, Patrick Brandao and 7 moreOpen Access, Chen Chen, Chen Qin and 8 moreOpen Access, Tao Wei, Angelica I. Aviles-Rivero and 5 moreOpen Access, Mohammad Alsharid, Yifan Cai and 4 moreOpen Access, Filip Rusak, Rodrigo Santa Cruz and 7 moreOpen Access, Mojtaba Lashgari, Nishant Ravikumar and 5 moreOpen Access, Francesco Masia, Walter Dewitte, Paola Borri, Wolfgang Langbein Open Access, David Schuhmacher, Stephanie Schrner and 10 moreOpen Access, Ardit Ramadani, Mai Bui and 4 moreOpen Access, Ivona Najdenkoska, Xiantong Zhen, Marcel Worring, Ling Shao Open Access, Chen Qin, Shuo Wang and 3 moreOpen Access, Arezoo Zakeri, Alireza Hokmabadi and 7 moreOpen Access, Alessia Atzeni, Loic Peter and 5 moreOpen Access, Juan Carlos ngeles Cern, Gilberto Ochoa Ruiz, Leonardo Chang, Sharib Ali Open Access, Xiebo Geng, Xiuli Liu, Shenghua Cheng, Shaoqun Zeng Open Access, Copyright 2022 Elsevier, except certain content provided by third parties, Cookies are used by this site. Thanks in advance for your time. Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. Supervised Synthesis Using Location-Sensitive Deep Network, 16.3. Research projects include: Brain MRI research (structural and DTI), CT and X-ray image analysis - automated detection to segmentation and characterization. We recommend authors see our open access page for further information. Professor, Department of Radiology and BRIC, UNC-Chapel Hill, USA, Editors: Kevin Zhou, Hayit Greenspan, Dinggang Shen, Sales tax will be calculated at check-out, Covers common research problems in medical image analysis and their challenges, Describes deep learning methods and the theories behind approaches for medical image analysis. His work focuses on developing computer vision and machine (deep) learning methods for automatic interpretation of medical imaging data for a variety of clinical applications with a recent focus on cancer. SCImago Journal Rank is an indicator, which measures the scientific influence of journals. Find out more on our funding arrangements page. To address the limitations of deep learning methods in medical image computing, this special issue solicits novel explainable/interpretable and generalizable deep learning methods for intelligent medical image computing applications. Cai W, et al. The Infona portal uses cookies, i.e. Cookie Notice ?& san francisco singapore sydney tokyo elsevier . Dr. Shens research interests include medical image analysis, computer vision, and pattern recognition. Deep Learning Models for Classifying Mammogram Exams Containing Unregistered Multi-View Images and Segmentation Maps of Lesions, 15. Open Access Articles - Medical Image Analysis - Journal - Elsevier Medical Image Analysis provides a forum for the dissemination of new research results in the field of medical and biological image analysis, with special emph Cerebral Microbleed Detection from MR Volumes, 7.1. Multi-Instance Multi-Stage Deep Learning for Medical Image Recognition, 5. Structured Regression for Robust Cell Detection Using Convolutional Neural Network, 8.2. . His research interests lie in computer vision and machine/deep learning and their applications to medical image analysis, face recognition and modeling, etc. Visit our open access page for full information. Article 102536. We cannot process tax exempt orders online. Visit our open access page for full information. Deep Voting: A Robust Approach Toward Nucleus Localization in Microscopy Images, 7.2. She is an Associate Editor for the IEEE Trans on Medical Imaging (TMI) journal. Authors can share their research in a variety of different ways and Elsevier has a number of green open access options available. Kings College London. [emailprotected]. Close. He has won multiple technology, patent and product awards, including R&D 100 Award and Siemens Inventor of the Year. Recently she was the Lead guest editor for an IEEE-TMI special Issue on "Deep Learning in Medical Imaging, May 2016. Deep Networks and Mutual Information Maximization for Cross-Modal Medical Image Synthesis, 17. For authors funded by those funders implementing Plan S principles from 2021, this effectively means that you can publish open access in this journal, receive funding for your Article Publishing Charges, and meet Plan S requirements. The most cited articles from Medical Image Analysis published since 2019, extracted from Scopus. University of Electronic Science and Technology of China. See also [ edit] Medical imaging Medical image computing Computer-assisted interventions The MICCAI Society Dr. George Mastorakis received his B.Eng (Honours) in Electronic Engineering from UMIST (University of Manchester Institute of Science & Technology), UK in 2000, his M.Sc. The Handbook of Medical Image Processing and Analysis is a comprehensive compilation of concepts and techniques used for processing and analyzing medical images after they have been generated or digitized. Deep Learning for Medical Image Analysis is a great learning resource for academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. young professionals in foreign policy; fluminense vs fortaleza prediction; biomedical signal processing projects October 26, 2022 We are continuously working with our author communities to select the best choice of license options, currently being defined for this journal as follows: Creative Commons Attribution-NonCommercial-NoDerivs (CC BY-NC-ND): for non-commercial purposes, lets others distribute and copy the article, and to include in a collective work (such as an anthology), as long as they credit the author(s) and provided they do not alter or modify the article. Theres no activation process to access eBooks; all eBooks are fully searchable, and enabled for copying, pasting, and printing. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc. Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching, 10. Characterization of Errors in Deep Learning-Based Brain MRI Segmentation, 11. All about Medical Image Analysis at Researcher.Life. Locality-constrained subcluster representation ensemble for lung image . Thanks in advance for your time. Get access to Medical Image Analysis details, facts, key metrics, recently published papers, top authors, submission guidelines all at one place. Enter STC215 at the checkout. Topics of interest include, but not limited to the following: Accepted papers are encouraged to demonstrate the effectiveness of the proposed deep learning methods to important clinical applications in collaboration with medical doctors. in Telecommunications from UCL (University College London), UK in 2001 and his Ph.D. in networking technologies from University of the Aegean, Greece in 2008. ARB=angiotensin . Save up to 30% on your own copy when you order via the Elsevier Store. If you decide to participate, a new browser tab will open so you can complete the survey after you have completed your visit to this website. Natural Language Processing for Large-Scale Medical Image Analysis Using Deep Learning, Copyright 2022 Elsevier, except certain content provided by third parties, Cookies are used by this site. However, they have significant limitations that make clinicians skeptical on their usefulness for clinical practice. Randomized Deep Learning Methods for Clinical Trial Enrichment and Design in Alzheimer's Disease, 16. Medical Image Analysis provides a forum for the dissemination of new research results in the field of medical and biological image analysis, with special emphasis on efforts related to the applications of computer vision, virtual reality and robotics to biomedical imaging problems. The methods should provide novel explainable/interpretable and generalizable solutions to key application domains such as disease classification and prediction, pathology detection and segmentation, image registration and reconstruction. Winners Deep Cascaded Networks for Sparsely Distributed Object Detection from Medical Images, 7.

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