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arXiv 2023年

作者： Bucholc, Magda James, Charlotte Al Khleifat, Ahmad Badhwar, AmanPreet Clarke, Natasha Dehsarvi, Amir Madan, Christopher R. Marzi, Sarah J. Shand, Cameron Schilder, Brian M. Tamburin, Stefano Tantiangco, Hanz M. Lourida, Ilianna Llewellyn, David J. Ranson, Janice M. Cognitive Analytics Research Lab School of Computing Engineering & Intelligent Systems Ulster University Derry United Kingdom NIHR Bristol Biomedical Research Centre University Hospitals Bristol Weston NHS Foundation Trust University of Bristol Bristol United Kingdom Department of Basic and Clinical Neuroscience Institute of Psychiatry Psychology & Neuroscience King's College London London United Kingdom Lab Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal Montréal Canada Institut de Génie Biomédical Université de Montréal Montréal Canada Département de Pharmacologie et Physiologie Université de Montréal Montréal Canada Aberdeen Biomedical Imaging Centre School of Medicine Medical Sciences and Nutrition University of Aberdeen Aberdeen United Kingdom School of Psychology University of Nottingham Nottingham United Kingdom UK Dementia Research Institute Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Department of Neurosciences Biomedicine and Movement Sciences University of Verona Verona Italy Information School University of Sheffield Sheffield United Kingdom University of Exeter Medical School Exeter United Kingdom The Alan Turing Institute London United Kingdom

Introduction: Machine learning (ML) has been extremely successful in identifying key features from high-dimensional datasets and executing complicated tasks with human expert levels of accuracy or greater. Methods: We summarize and critically evaluate current applications of ML in dementia research and highlight directions for future research. Results: We present an overview of ML algorithms most frequently used in dementia research and highlight future opportunities for the use of ML in clinical practice, experimental medicine, and clinical trials. We discuss issues of reproducibility, replicability and interpretability and how these impact the clinical applicability of dementia research. Finally, we give examples of how state-of-the-art methods, such as transfer learning, multi-task learning, and reinforcement learning, may be applied to overcome these issues and aid the translation of research to clinical practice in the future. Discussion: ML-based models hold great promise to advance our understanding of the underlying causes and pathological mechanisms of dementia. © 2023, CC BY.

关键词： Clinical research

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A multivariate MRI model of Alzheimer’s disease risk is associated with clinical diagnosis, PET imaging, and plasma biomarkers in a mixed dementia sample

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Alzheimer's & Dementia 2023年第S10期19卷

作者： Jeffrey S Phillips Sindhuja Tirumalai Govindarajan Gyujoon Hwang Guray Erus Katheryn A Q Cousins Sandhitsu R. Das David A. Wolk David J. Irwin Murray Grossman Ilya M. Nasrallah Christos Davatzikos Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Centre for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Penn Alzheimer’s Disease Research Center University of Pennsylvania Philadelphia PA USA Frontotemporal Degeneration Center Department of Neurology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Pennsylvania Philadelphia PA USA

Background We sought to validate two structural MRI-based brain health models: Spatial Pattern of Atrophy for REcognition of Alzheimer’s disease (SPARE-AD) and SPARE-Brain Age Gap (SPARE-BAG), which estimates the discrepancy between biological brain age and chronological age. These models' generalization to non-amnestic AD (naAD) syndromes and associations with positron emission tomography (PET) and plasma biomarkers remain underinvestigated. We hypothesized that SPARE-AD scores in naAD would be lower (less abnormal) than in amnestic AD (aAD) but elevated (more abnormal) relative to controls; and that SPARE-AD but not SPARE-BAG scores would be associated with PET and plasma biomarkers of AD. Method SPARE-AD and SPARE-BAG scores were estimated for each of 1607 MRI scans from 277 cognitively normal adults (62.8% female; mean age = 69.5 years) and 902 clinical participants (54.2% female; mean age = 73.1 years), including 585 with amnestic mild cognitive impairment (aMCI) or aAD; 56 with naAD syndromes; 28 with vascular disease; 28 with Lewy body disorders (LBD); 18 with suspected frontotemporal lobar degeneration (FTLD); 56 with non-amnestic MCI of uncertain etiology; and 131 with other clinical diagnoses. We used mixed effects models adjusting for age, sex, and global cognition to test group differences in SPARE-AD and SPARE-BAG. Additionally, we assessed associations with tau (n = 122) and amyloid-beta (n = 199) PET imaging and plasma-based phosphorylated tau-181 (p-tau181) and glial fibrillary acidic protein (GFAP) (n = 358). Result SPARE-AD scores were elevated vs. controls in aMCI/aAD, naAD, vascular disease, FTLD, and naMCI (all p<0.003) but not in LBD or other diagnoses (p>0.1). NaAD patients had lower SPARE-AD (p<0.02) but higher SPARE-BAG scores (p<0.02) than aMCI/aAD patients. SPARE-AD scores were positively associated with mean cortical tau PET uptake, amyloid PET positivity, and plasma p-tau181 and GFAP (all p<0.001). In contrast, SPARE-BAG scores were not

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Mitosis domain generalization in histopathology images - The MIDOG challenge

arXiv

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arXiv 2022年

作者： Aubreville, Marc Stathonikos, Nikolas Bertram, Christof A. Klopfleisch, Robert ter Hoeve, Natalie Ciompi, Francesco Wilm, Frauke Marzahl, Christian Donovan, Taryn A. Maier, Andreas Breen, Jack Ravikumar, Nishant Chung, Youjin Park, Jinah Nateghi, Ramin Pourakpour, Fattaneh Fick, Rutger H.J. Hadj, Saima Ben Jahanifar, Mostafa Rajpoot, Nasir Dexl, Jakob Wittenberg, Thomas Kondo, Satoshi Lafarge, Maxime W. Koelzer, Viktor H. Liang, Jingtang Wang, Yubo Long, Xi Liu, Jingxin Razavi, Salar Khademi, April Yang, Sen Wang, Xiyue Veta, Mitko Breininger, Katharina Technische Hochschule Ingolstadt Ingolstadt Germany Pathology Department UMC Utrecht Netherlands Institute of Pathology University of Veterinary Medicine Vienna Austria Institute of Veterinary Pathology Freie Universität Berlin Berlin Germany Computational Pathology Group Radboud UMC Nijmegen Netherlands Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Germany Department of Anatomic Pathology Schwarzman Animal Medical Center New York United States CISTIB Center for Computational Imaging and Simulation Technologies in Biomedicine School of Computing University of Leeds United Kingdom Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Electrical and Electronics Engineering Department Shiraz University of Technology Shiraz Iran Tehran Iran Tribun Health Paris France Tissue Image Analytics Centre Department of Computer Science University of Warwick United Kingdom Fraunhofer-Institute for Integrated Circuits IIS Erlangen Germany Muroran Institute of Technology Hokkaido Japan Department of Pathology and Molecular Pathology University Hospital University of Zurich Zurich Switzerland School of Life Science and Technology Xidian University Shannxi China Histo Pathology Diagnostic Center Shanghai China Xi'an Jiaotong-Liverpool University Suzhou China Electrical Computer and Biomedical Engineering Ryerson University TorontoON Canada Tencent AI Lab Shenzhen518057 China College of Computer Science Sichuan University Chengdu610065 China Medical Image Analysis Group TU Eindhoven Netherlands Department of Artificial Intelligence in Biomedical Engineering Friedrich-Alexander-Universität Erlangen-Nürnberg Germany

The density of mitotic figures within tumor tissue is known to be highly correlated with tumor proliferation and thus is an important marker in tumor grading. Recognition of mitotic figures by pathologists is known to be subject to a strong inter-rater bias, which limits the prognostic value. State-of-the-art deep learning methods can support the expert in this assessment but are known to strongly deteriorate when applied in a different clinical environment than was used for training. One decisive component in the underlying domain shift has been identified as the variability caused by using different whole slide scanners. The goal of the MICCAI MIDOG 2021 challenge has been to propose and evaluate methods that counter this domain shift and derive scanner-agnostic mitosis detection algorithms. The challenge used a training set of 200 cases, split across four scanning systems. As a test set, an additional 100 cases split across four scanning systems, including two previously unseen scanners, were given. The best approaches performed on an expert level, with the winning algorithm yielding an F1 score of 0.748 (CI95: 0.704-0.781). In this paper, we evaluate and compare the approaches that were submitted to the challenge and identify methodological factors contributing to better performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Scanning

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Cross-scale functional connectivity patterns of the aging brain learned from the multi-cohort iSTAGing study

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Alzheimer's & Dementia 2023年第S17期19卷

作者： Zhen Zhou Hongming Li Chau B Tran Yuncong Ma Dhivya Srinivasan Ahmed Abdulkadir Junhao Wen Guray Erus Elizabeth Mamourian Ilya M. Nasrallah Nick Bryan David A. Wolk Lori L Beason-Held Susan M. Resnick Haochang Shou Christos Davatzikos Yong Fan University of Pennsylvania philadelphia PA USA Perelman School of Medicine University of Pennsylvania Philadelphia PA USA University of Pennsylvania Philadelphia PA USA Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA Laboratory for Research in Neuroimaging Department of Clinical Neurosciences Lausanne University Hospital (CHUV) and University of Lausanne Lausanne Switzerland Centre for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Pennsylvania Philadelphia PA USA Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA University of Texas at Austin Austin TX USA Department of Pathology and Laboratory Medicine Alzheimer’s Disease Center Perelman School of Medicine University of Pennsylvania Philadelphia PA USA National Institute on Aging Baltimore MD USA Laboratory of Behavioral Neuroscience National Institute on Aging Baltimore MD USA Department of Biostatistics Epidemiology & Informatics University of Pennsylvania Philadelphia PA USA Correspondece

Background Brain functional connectivity (FC) measures derived from resting-state fMRI (rsfMRI) data have advanced our understanding of the brain organization. However, most existing studies investigate the brain functional networks (FNs) at a specific scale, ignoring potentially complementary FC information at different levels of spatial granularity as well as the cross-scale information. Given that the brain is a multi-scale, hierarchical system, FCs between FNs at different spatial scales may provide complementary information to those measured within-scales for characterizing the aging brain. Method We computed multiscale FNs to capture their FC measures across multiple scales and investigated the associations between cross-scale FCs and the brain aging based on rsfMRI data of a diverse cohort (n = 4186, aged 22 to 97 years) from seven sites of the iSTAGING study [1]. For each subject, we computed the FNs at seven scales, yielding seven sets of k (k = 17, 25, 50, 75, 100, 125 and 150) FNs, same as our previous study [2-3]. We estimated FC measures between the 542 FNs using Pearson’s correlation, yielding the cross-scale FC matrices that were harmonized across sites in the tangent space using ComBat-GAM [4] with sex and age as covariates. The harmonized FC measures were used as features to build an age prediction model using Ridge regression as illustrated in Figure 1. The brain age prediction model was compared with that built on harmonized FC measures between FNs of the same scales, with the performance estimated with a nested five-fold cross-validation. Result The cross-scale FC measures performed significantly better than the within-scale FC measures for the brain age prediction, as summarized in Table 1. Figure 2 shows the most informative FC measures for the brain age prediction. Specifically, FC measures computed both cross- and within-scales between the visual, somatomotor, and frontoparietal networks provided relatively more useful information for the bra

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NIMG-29. ASSOCIATION OF PARTIAL T2-FLAIR MISMATCH SIGN AND ISOCITRATE DEHYDROGENASE MUTATION IN WHO GRADE 4 GLIOMA/GLIOBLASTOMA: RESULTS FROM THE RESPOND CONSORTIUM

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Neuro-Oncology 2022年第SUPPLEMENT_7期24卷 vii168–vii169页

作者： Lee, Matthew Sako, Chiharu Patel, Sohil Mohan, Suyash Balana, Carmen Barnholtz-Sloan, Jill Sloan, Andrew Badve, Chaitra Poisson, Laila Griffith, Brent Booth, Thomas Palmer, Joshua Chakravarti, Arnab Bakas, Spyridon Nasrallah, MacLean Choi, Yoon Seong Dicker, Adam Flanders, Adam Shi, Wenyin Mahajan, Abhishek Colen, Rivka Marcus, Daniel Orringer, Daniel Davatzikos, Christos Jain, Rajan Department of Radiology NYU Grossman School of Medicine New York NY USA Center for Biomedical Image Computing and Analytics and Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia USA Department of Radiology University of Virginia School of Medicine Charlottesville USA Center for Biomedical Image Computing and Analytics Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Medical Oncology Department Catalan Institute of Oncology Barcelona Spain Center for Biomedical Informatics and Information Technology and Division of Cancer Epidemiology and Genetics National Cancer Institute Bethesda MD USA Department of Pathology and Department of Neurosurgery Case Western Reserve University and University Hospitals Cleveland Medical Center Seidman Cancer Center and Case Comprehensive Cancer Center Cleveland USA Department of Radiology Case Western Reserve University and University Hospitals Cleveland Medical Center Cleveland USA Department of Public Health Sciences Center for Bioinformatics Henry Ford Health System Detroit MI USA Department of Radiology Henry Ford Health System Detroit MI USA School of Biomedical Engineering and Imaging Sciences King’s College London United Kingdom The Department of Radiation Oncology The James Cancer Hospital Ohio State University Wexner Medical Center Columbus OH USA Department of Radiation Oncology Ohio State University Wexner Medical Center Columbus OH USA Center for Biomedical Image Computing and Analytics Department of Radiology and Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Yonsei University College of Medicine Seoul Republic of Korea Department of Radiation Oncology Sidney Kimmel Cancer Center Thomas Jefferson

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Why is the Winner the Best?

Why is the Winner the Best?

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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WHITE MATTER HYPERINTENSITIES IN RELATION TO PATTERNS OF ACCELERATED BRAIN AGING, AD-LIKE ATROPHY AND AMYLOID BURDEN: RESULTS FROM THE ISTAGING CONSORTIUM ON MACHINE LEARNING AND LARGE-SCALE IMAGING analytics

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Alzheimer's & Dementia 2019年第7期15卷 P904-P905页

作者： Mohamad Habes Raymond Pomponio Haochang Shou Jimit Doshi Aristeidis Sotiras Guray Erus Lenore J. Launer Elizabeth Mamourian Murat Bilgel Kristine Yaffe Dhivya Srinivasan Mark A. Espeland Ilya M. Nasrallah Christopher C. Rowe Henry Voelzke Sterling C. Johnson Marilyn S. Albert Nick Bryan Hans J. Grabe Susan M. Resnick Christos Davatzikos University of Pennsylvania Philadelphia PA USA Department of Neurology and Penn Memory Center University of Pennsylvania Philadelphia PA USA Center for Biomedical Image Computing and Analytics and Department of Radiology University of Pennsylvania Philadelphia PA USA Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA National Institute on Aging Bethesda MD USA National Institute on Aging NIH Baltimore MD USA Department of Epidemiology & Biostatistics University of California San Francisco San Francisco CA USA Wake Forest School of Medicine Winston-Salem NC USA Department of Molecular Imaging and Therapy Centre for PET Austin Health Heidelberg Australia Institute for Community Medicine University Medicine Greifswald Greifswald Germany Wisconsin Alzheimer's Institute University of Wisconsin School of Medicine and Public Health Madison WI USA Johns Hopkins University School of Medicine Baltimore MD USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA University of Texas at Austin Austin TX USA Department of Psychiatry University Medicine Greifswald and German Center for Neurodegenerative Disease (DZNE) Rostock/Greifswald Greifswald Germany

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The Alzheimer's Disease Prediction of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

arXiv

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arXiv 2020年

作者： Marinescu, Razvan V. Oxtoby, Neil P. Young, Alexandra L. Bron, Esther E. Toga, Arthur W. Weiner, Michael W. Barkhof, Frederik Fox, Nick C. Eshaghi, Arman Ansart, Manon Durrleman, Stanley Lu, Pascal Iddi, Samuel Li, Dan Thompson, Wesley K. Donohue, Michael C. Nahon, Aviv Levy, Yarden Halbersberg, Dan Cohen, Mariya Liao, Huiling Li, Tengfei Yu, Kaixian Zhu, Hongtu Tamez-Peña, José G. Ismail, Aya Wood, Timothy Bravo, Hector Corrada Nguyen, Minh Sun, Nanbo Feng, Jiashi Thomas Yeo, B.T. Chen, Gang Qi, Ke Chen, Shiyang Qiu, Deqiang Buciuman, Ionut Kelner, Alex Pop, Raluca Rimocea, Denisa Ghazi, Mostafa M. Nielsen, Mads Ourselin, Sebastien Sørensen, Lauge Venkatraghavan, Vikram Liu, Keli Rabe, Christina Manser, Paul Hill, Steven M. Howlett, James Huang, Zhiyue Kiddle, Steven Mukherjee, Sach Rouanet, Anaïs Taschler, Bernd Tom, Brian D.M. White, Simon R. Faux, Noel Sedai, Suman de Velasco Oriol, Javier Clemente, Edgar E.V. Estrada, Karol Aksman, Leon Altmann, Andre Stonnington, Cynthia M. Wang, Yalin Wu, Jianfeng Devadas, Vivek Fourrier, Clementine Raket, Lars Lau Sotiras, Aristeidis Erus, Guray Doshi, Jimit Davatzikos, Christos Vogel, Jacob Doyle, Andrew Tam, Angela Diaz-Papkovich, Alex Jammeh, Emmanuel Koval, Igor Moore, Paul Lyons, Terry J. Gallacher, John Tohka, Jussi Ciszek, Robert Jedynak, Bruno Pandya, Kruti Bilgel, Murat Engels, William Cole, Joseph Golland, Polina Klein, Stefan Alexander, Daniel C. Centre for Medical Image Computing University College London United Kingdom Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States Biomedical Imaging Group Rotterdam Department of Radiology and Nuclear Medicine Erasmus MC Netherlands Laboratory of NeuroImaging University of Southern California United States Center for Imaging of Neurodegenerative Diseases University of California San Francisco United States Department of Radiology and Nuclear Medicine VU Medical Centre Netherlands Dementia Research Centre The UK Dementia Research Institute UCL Queen Square Institute of Neurology United Kingdom Institut du Cerveau et de la Moelle épinière Paris France Alzheimer's Therapeutic Research Institute University of Southern California United States Department of Statistics and Actuarial Science University of Ghana Ghana Department of Family Medicine and Public Health University of California San Diego United States Ben Gurion University of the Negev Beersheba Israel The University of Texas Health Science Center at Houston Houston United States Instituto Tecnológico y de Estudios Superiores de Monterrey Monterrey Mexico University of Maryland College Park United States National University of Singapore Singapore Singapore Medical College of Wisconsin Milwaukee United States Emory University Atlanta United States Georgia Institute of Technology Atlanta United States Vasile Lucaciu National College Baia Mare Romania Biomediq A/S Denmark Cerebriu A/S Denmark University of Copenhagen Denmark School of Biomedical Engineering and Imaging Sciences King's College London United Kingdom Genentech United States MRC Biostatistics Unit University of Cambridge United Kingdom IBM Research Australia Melbourne Australia Brandeis University Waltham United States Mayo Clinic ScottsdaleAZ United States H. Lundbeck A/S Denmark Clinical Memory Research Unit Department of Clinical Sciences Malmo Lund University Lund Sweden Center

Accurate prediction of progression in subjects at risk of Alzheimer's disease is crucial for enrolling the right subjects in clinical trials. However, a prospective comparison of state-of-the-art algorithms for predicting disease onset and progression is currently lacking. We present the findings of The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a prediction, for each month of a 5-year future time period, of three key outcomes: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. The methods used by challenge participants included multivariate linear regression, machine learning methods such as support vector machines and deep neural networks, as well as disease progression models. No single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperform simple baselines in predictive ability. However, for ADASCog13 no single submitted prediction method was significantly better than random guesswork. Two ensemble methods based on taking the mean and median over all predictions, obtained top scores on almost all tasks. Better than average performance at diagnosis prediction was generally associated with the additional inclusion of features from cerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as the slope or maxima/minima of patient-specific biomarkers. On a limited, cross-sectional subset of the data emulating clinical trials, performance of the best algorithms at predicting clinical diagnosis decreased only slightly (2 perce

关键词： Forecasting

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***: Dissemination platform for deep learning models

arXiv

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arXiv 2019年

作者： Hosny, Ahmed Schwier, Michael Berger, Christoph Örnek, Evin P. Turan, Mehmet Tran, Phi V. Weninger, Leon Isensee, Fabian Maier-Hein, Klaus H. McKinley, Richard Lu, Michael T. Hoffmann, Udo Menze, Bjoern Bakas, Spyridon Fedorov, Andriy Aerts, Hugo J.W.L. Program Brigham and Women's Hospital Harvard Medical School BostonMA United States Department of Radiation Oncology Dana-Farber Cancer Institute Harvard Medical School BostonMA United States Department of Radiology Brigham and Women's Hospital Dana-Farber Cancer Institute Harvard Medical School BostonMA United States Harvard Medical School BostonMA United States Institute for Advanced Study Department of Informatics Technical University of Munich Munich Germany Max Planck Institute for Intelligent Systems Stuttgart Germany Booz Allen Hamilton McLeanVA United States Institute of Imaging & Computer Vision RWTH Aachen University Aachen Germany Heidelberg Germany Support Centre for Advanced Neuroimaging University Institute of Diagnostic Interventional Neuroradiology Inselspital Bern University Hospital Bern Switzerland Cardiovascular Imaging Research Center Massachusetts General Hospital Harvard Medical School Boston United States Cardiac MR PET CT Program Department of Radiology Massachusetts General Hospital BostonMA United States Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Radiology & Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Radiology and Nuclear Medicine GROW CARIM Maastricht University Maastricht Netherlands

Recent advances in artificial intelligence research have led to a profusion of studies that apply deep learning to problems in image analysis and natural language processing among others. Additionally, the availability of open-source computational frameworks has lowered the barriers to implementing state-of-the-art methods across multiple domains. Albeit leading to major performance breakthroughs in some tasks, effective dissemination of deep learning algorithms remains challenging, inhibiting reproducibility and benchmarking studies, impeding further validation, and ultimately hindering their effectiveness in the cumulative scientific progress. In developing a platform for sharing research outputs, we present *** (***), a community-driven container-based software engine and platform for the structured dissemination of deep learning models. For contributors, the engine controls data flow throughout the inference cycle, while the contributor-facing standard template exposes model-specific functions including inference, as well as pre- and post-processing. Python and RESTful Application programming interfaces (APIs) enable users to interact with models hosted on *** and allows both researchers and developers to utilize models out-of-the-box. *** is domain-, data-, and framework-agnostic, catering to different workflows and contributors' preferences. Copyright © 2019, The Authors. All rights reserved.

关键词： Containers

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Discovering Alzheimer’s disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering

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Alzheimer's & Dementia 2023年第S17期19卷

作者： Zhijian Yang Junhao Wen Ahmed Abdulkadir Yuhan Cui Guray Erus Elizabeth Mamourian Randa Melhem Dhivya Srinivasan Sindhuja Tirumalai Govindarajan Jiong Chen Mohamad Habes Colin L Masters Paul Maruff Jurgen Fripp Luigi Ferrucci Marilyn S. Albert Sterling C Johnson John C Morris Pamela LaMontagne Daniel S. Marcus Tammie L.S. Benzinger David A. Wolk Li Shen Jingxuan Bao Susan M. Resnick Duygu Tosun Haochang Shou Ilya M. Nasrallah Christos Davatzikos the Alzheimer’s Disease Neuroimaging Initiative, the Preclinical AD Consortium, the Baltimore Longitudinal Study of Aging, and the iSTAGING study 1Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Correspondece Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA University of Bern Bern Switzerland Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA Centre for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA University of Texas Health San Antonio San Antonio TX USA Florey Institute of Neuroscience and Mental Health Parkville VIC Australia The Florey Institute of Neuroscience and Mental Health University of Melbourne Parkville VIC Australia CSIRO Health and Biosecurity Australian E-Health Research Centre Brisbane QLD Australia National Institute on Aging NIH Baltimore MD USA Department of Neurology Johns Hopkins University School of Medicine Baltimore MD USA Wisconsin Alzheimer’s Institute University of Wisconsin-Madison School of Medicine and Public Health Madison WI USA Knight Alzheimer Disease Research Center St. Louis MO USA Washington University School of Medicine Saint Louis MO USA Washington University School of Medicine St. Louis MO USA Department of Neurology University of Pennsylvania School of Medicine Philadelphia PA USA Institute for Biomedical Informatics University of Pennsylvania ATHENS PA USA University of Pennsylvania Philadelphia PA USA Laboratory of Behavioral Neuroscience National Institute on Aging Baltimore MD USA Department of Radiology and Biomedical Imaging University of Cal

Background The heterogeneity of neurodegenerative diseases, including Alzheimer’s disease (AD), has hampered precision diagnosis and treatment. Machine learning methods enable the identification of genetically-explained disease subtypes with distinct brain phenotypes. Method Gene-SGAN is a novel, multi-view, weakly-supervised deep clustering method that jointly considers phenotypic and genetic data, thereby deriving subtypes of disease-related brain changes with genetic associations. We apply the method to derive subtypes related to AD. Analyses used structural MRIs and whole-genome sequencing data from the Alzheimer’s Disease Neuroimaging Initiative (1,533 participants, including controls and patients with Mild Cognitive Impairment (MCI) and AD). Result Gene-SGAN identified four distinct subtypes within MCI/AD: A1, normal anatomy; A2, focal medial temporal lobe (MTL) atrophy; A3, severe atrophy in neocortex and MTL; A4, severe cortical but relatively modest MTL atrophy (Figure 1). The four subtypes reveal significant differences in seven known AD-related genetic variants (Figure 2). The identified subtype-associated SNPs not only resemble previous findings, but also suggest genetic protective effects contributing to the observed resilience (A1), potential inflammatory mechanisms underlying subtypes (A2&A3), and the effect of SNPs on atypical atrophy patterns of AD (A4). Clinically, A1 and A3 participants showed the best and the worst cognitive performances (Figure 3). A3 participants had the most abnormal CSF Aβ (p<0.001 vs. A1&A4 and p = 0.039 vs. A2), and exhibited the highest white matter hyperintensity (WMH) volumes (p<0.001 vs. A1&A4 and p = 0.017 vs. A2). However, A2 participants showed significantly higher CSF phospho-tau than A3 participants (p = 0.019). A4 participants were the youngest group (p<0.001 vs. all other groups), suggesting the inclusion of early onset AD participants, who have a higher prevalence of hippocampal-sparing variants such as posterio

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