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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Sha, Jiahang Bao, Jingxuan Liu, Kefei Yang, Shu Wen, Zixuan Cui, Yuhan Wen, Junhao Davatzikos, Christos Moore, Jason H. Saykin, Andrew J. Long, Qi Shen, Li University of Pennsylvania Epidemiology and Informatics Department of Biostatistics Philadelphia United States Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China University of Pennsylvania Center for Biomedical Image Computing and Analytics Philadelphia United States Cedars-Sinai Medical Center Department of Computational Biomedicine West Hollywood United States Indiana University Department of Radiology and Imaging Sciences Indianapolis United States

ISBN: (纸本)9781665468190

Investigating the relationship between genetic variation and phenotypic traits is a key issue in quantitative genetics. Specifically for Alzheimer's disease, the association between genetic markers and quantitative traits remains vague while, once identified, will provide valuable guidance for the study and development of genetic-based treatment approaches. Currently, to analyze the association of two modalities, sparse canonical correlation analysis (SCCA) is commonly used to compute one sparse linear combination of the variable features for each modality, giving a pair of linear combination vectors in total that maximizes the cross-correlation between the analyzed modalities. One drawback of the plain SCCA model is that the existing findings and knowledge cannot be integrated into the model as priors to help extract interesting correlation as well as identify biologically meaningful genetic and phenotypic markers. To bridge this gap, we introduce preference matrix guided SCCA (PM-SCCA) that not only takes priors encoded as a preference matrix but also maintains computational simplicity. A simulation study and a real-data experiment are conducted to investigate the effectiveness of the model. Both experiments demonstrate that the proposed PM-SCCA model can capture not only genotype-phenotype correlation but also relevant features effectively. © 2022 IEEE.

关键词： Matrix algebra

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Foundation Models in Computational Pathology: A Review of Challenges, Opportunities, and Impact

arXiv

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arXiv 2025年

作者： Bilal, Mohsin Aadam Raza, Manahil Altherwy, Youssef Alsuhaibani, Anas Abduljabbar, Abdulrahman Almarshad, Fahdah Golding, Paul Rajpoot, Nasir Information Systems Department College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia Luddy School of Informatics Computing and Engineering Indiana University Indianapolis United States Tissue Image Analytics Centre Department of Computer Science University of Warwick Coventry United Kingdom Frontier AI San Francisco Bay Area United States

From self-supervised, vision-only models to contrastive visual-language frameworks, computational pathology has rapidly evolved in recent years. Generative AI "co-pilots" now demonstrate the ability to mine subtle, sub-visual tissue cues across the cellular-to-pathology spectrum, generate comprehensive reports, and respond to complex user queries. The scale of data has surged dramatically, growing from tens to millions of multi-gigapixel tissue images, while the number of trainable parameters in these models has risen to several billion. The critical question remains: how will this new wave of generative and multi-purpose AI transform clinical diagnostics? In this article, we explore the true potential of these innovations and their integration into clinical practice. We review the rapid progress of foundation models in pathology, clarify their applications and significance. More precisely, we examine the very definition of foundational models, identifying what makes them foundational, general, or multipurpose, and assess their impact on computational pathology. Additionally, we address the unique challenges associated with their development and evaluation. These models have demonstrated exceptional predictive and generative capabilities, but establishing global benchmarks is crucial to enhancing evaluation standards and fostering their widespread clinical adoption. In computational pathology, the broader impact of frontier AI ultimately depends on widespread adoption and societal acceptance. While direct public exposure is not strictly necessary, it remains a powerful tool for dispelling misconceptions, building trust, and securing regulatory support. © 2025, CC BY-NC-SA.

关键词： Visual languages

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Mutually-Constrained Cross-Sectional and Longitudinal Non-Negative Matrix Factorization: Application to Modeling Brain Aging Trajectories

Mutually-Constrained Cross-Sectional and Longitudinal Non-Ne...

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IEEE International Symposium on Biomedical Imaging

作者： Ioanna Skampardoni Junhao Wen Erus Guray Haochang Shou Konstantina Nikita Christos Davatzikos Centre for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Laboratory of AI and Biomedical Science (LABS) Stevens Neuroimaging and Informatics Institute Keck School of Medicine of USC University of Southern California Los Angeles CA USA Department of Biostatistics Epidemiology & Informatics Penn Statistics in Imaging and Visualization Center University of Pennsylvania Philadelphia PA USA

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Brain aging is a multifaceted and highly heterogeneous process accompanied by several pathologies. Here, we propose a method for dissecting the heterogeneity of neuropathologic processes occurring with aging using machine learning and leveraging information from cross-sectional and longitudinal data. Specifically, we hypothesize that the heterogeneity observed in brain aging can be captured by a set of patterns consistent with longitudinal trajectories of brain change, the latter directly capturing evolving neuropathologic processes on an individual basis. Applying the method to structural magnetic resonance imaging data from the BLSA study, we derived five distinct, reproducible, and clinically informative components of neuroanatomical brain change, highlighting the method’s potential as a tool for precision medicine.

关键词： Pathology Magnetic resonance imaging Precision medicine Sociology Aging Hazards Trajectory

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Preference Matrix Guided Sparse Canonical Correlation Analysis for Genetic Study of Quantitative Traits in Alzheimer’s Disease

Preference Matrix Guided Sparse Canonical Correlation Analys...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jiahang Sha Jingxuan Bao Kefei Liu Shu Yang Zixuan Wen Yuhan Cui Junhao Wen Christos Davatzikos Jason H. Moore Andrew J. Saykin Qi Long Li Shen Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Philadelphia USA Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia USA Department of Computational Biomedicine Cedars-Sinai Medical Center West Hollywood USA Department of Radiology and Imaging Sciences Indiana University Indianapolis USA

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Investigating the relationship between genetic variation and phenotypic traits is a key issue in quantitative genetics. Specifically for Alzheimer’s disease, the association between genetic markers and quantitative traits remains vague while, once identified, will provide valuable guidance for the study and development of genetic-based treatment approaches. Currently, to analyze the association of two modalities, sparse canonical correlation analysis (SCCA) is commonly used to compute one sparse linear combination of the variable features for each modality, giving a pair of linear combination vectors in total that maximizes the cross-correlation between the analyzed modalities. One drawback of the plain SCCA model is that the existing findings and knowledge cannot be integrated into the model as priors to help extract interesting correlation as well as identify biologically meaningful genetic and phenotypic markers. To bridge this gap, we introduce preference matrix guided SCCA (PM-SCCA) that not only takes priors encoded as a preference matrix but also maintains computational simplicity. A simulation study and a real-data experiment are conducted to investigate the effectiveness of the model. Both experiments demonstrate that the proposed PM-SCCA model can capture not only genotype-phenotype correlation but also relevant features effectively.

关键词： Analytical models Correlation Biological system modeling Computational modeling Genetics Feature extraction Data models

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MAGIC: Multi-scale Heterogeneity Analysis and Clustering for Brain Diseases 23rd

MAGIC: Multi-scale Heterogeneity Analysis and Clustering for...

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23rd International Conference on Medical image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Wen, Junhao Varol, Erdem Chand, Ganesh Sotiras, Aristeidis Davatzikos, Christos Center for Biomedical Image Computing and Analytics Perelman School of Medicine University of Pennsylvania Philadelphia United States Department of Statistics Center for Theoretical Neuroscience Zuckerman Institute Columbia University New York United States Department of Radiology School of Medicine Washington University in St. Louis St. Louis United States Department of Radiology and Institute for Informatics Washington University School of Medicine St. Louis United States

ISBN: (纸本)9783030597276

There is a growing amount of clinical, anatomical and functional evidence for the heterogeneous presentation of neuropsychiatric and neurodegenerative diseases such as schizophrenia and Alzheimers Disease (AD). Elucidating distinct subtypes of diseases allows a better understanding of neuropathogenesis and enables the possibility of developing targeted treatment programs. Recent semi-supervised clustering techniques have provided a data-driven way to understand disease heterogeneity. However, existing methods do not take into account that subtypes of the disease might present themselves at different spatial scales across the brain. Here, we introduce a novel method, MAGIC, to uncover disease heterogeneity by leveraging multi-scale clustering. We first extract multi-scale patterns of structural covariance (PSCs) followed by a semi-supervised clustering with double cyclic block-wise optimization across different scales of PSCs. We validate MAGIC using simulated heterogeneous neuroanatomical data and demonstrate its clinical potential by exploring the heterogeneity of AD using T1 MRI scans of 228 cognitively normal (CN) and 191 patients. Our results indicate two main subtypes of AD with distinct atrophy patterns that consist of both fine-scale atrophy in the hippocampus as well as large-scale atrophy in cortical regions. The evidence for the heterogeneity is further corroborated by the clinical evaluation of two subtypes, which indicates that there is a subpopulation of AD patients that tend to be younger and decline faster in cognitive performance relative to the other subpopulation, which tends to be older and maintains a relatively steady decline in cognitive abilities. © 2020, Springer Nature Switzerland AG.

关键词： Neurodegenerative diseases

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Prediction of functional therapeutic response to thrombectomy in acute stroke using neural networks

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Clinical Neurophysiology 2024年 159卷 e5-e5页

作者： Stoll, K. von Braun, M. Kürsten, D. Welle, F. Wawrzyniak, M. Groos, A. Klingbeil, J. Stockert, A. Kaiser, D.P.O. Kellner, E. Reisert, M. Hoffmann, K. Scheuermann, G. Gillmann, C. Saur, D. Neuroimaging Laboratory Department of Neurology University of Leipzig Medical Center Leipzig Germany Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Leipzig University Leipzig Germany Institute of Neuroradiology University Medical Center Carl Gustav Carus Dresden Germany Medical Physics Department of Diagnostic and Interventional Radiology Medical Center – University of Freiburg Freiburg Germany Institute of Neuroradiology University of Leipzig Medical Center Leipzig Germany Image and Signal Processing Group Institute for Informatics Leipzig University Leipzig Germany

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Novel genomic loci shape two neuroanatomical dimensions of AD in non-demented populations

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Alzheimer's & Dementia 2023年第S10期19卷

作者： Junhao Wen Zhijian Yang Ioanna Skampardoni Marilena De Pian Guray Erus Elizabeth Mamourian Yuhan Cui Bingxin Zhao Ilya M. Nasrallah Arthur W. Toga Haochang Shou Li Shen Christos Davatzikos 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 Pennsylvania philadelphia PA USA Upenn Philadelphia PA USA University of Pennsylvania Philadelphia PA USA Laboratory of Neuro Imaging (LONI) University of Southern California Los Angeles CA USA Department of Biostatistics Epidemiology & Informatics University of Pennsylvania Philadelphia PA USA University of Pennsylvania Philadelphia PA Greece

Background Artificial intelligence has been in burgeoning demand to be applied to magnetic imaging resonance (MRI) data to parse the neuroanatomical heterogeneity of Alzheimer’s disease (AD) and/or mild cognitive impairment (MCI) (Wen et al., 2021a, 2021b; Yang et al., 2021). However, the genetic heterogeneity leading to this neuroanatomical heterogeneity, especially at the early asymptomatic stages, is not fully elucidated. Method A deep semi-supervised representation learning method, called Surreal-GAN (Yang et al., 2022), was applied to T1-weighted MRI from AD/MCI patients from ADNI to define the two neuroanatomical dimensions of AD at the symptomatic stages. The R1 dimension was characterized by widespread brain atrophy without an exclusive focus on the medial temporal lobe, whereas the R2 dimension showed focal medial temporal lobe atrophy (Wen et al., 2022). We then applied the trained model to 33,540 non-demented individuals from UK Biobank. Imputed genotyping data were analyzed in genome-wide association studies (GWAS). We performed multiple linear regressions controlling for confounding factors (e.g., age) via Plink (Purcell et al., 2007). We defined a genomic locus (represented by the top lead SNP) considering linkage disequilibrium. Finally, we manually queried all leading SNPs on GWAS Catalog (Buniello et al., 2019) to determine if the genomic loci were novel, provided they were not previously associated with any clinical traits. Result GWAS discovered 24 genomic loci, of which 7 were novel for R1 and R2, respectively (Fig. 1). For R1, 8 mapped genes, based on physical positions, eQTL, and chromatin interactions, were associated with AD-related traits in the literature. For example, the KDM2B gene from the genomic locus (rs9795600, P-value = 1.13e-08) was previously associated with AD (Gouveia et al., 2022). For R2, 13 mapped genes were associated with AD-related traits. For example, the TESC gene from the genomic locus (rs77956314, P-value = 9.99e-29) w

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Disentangling brain heterogeneity via semi-supervised deep-learning and MRI: Dimensional representations of Alzheimer's disease

arXiv

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arXiv 2021年

作者： Yang, Zhijian Nasrallah, Ilya M. Shou, Haochang Wen, Junhao Doshi, Jimit Habes, Mohamad Erus, Guray Abdulkadir, Ahmed Resnick, Susan M. Wolk, David Davatzikos, Christos Department of Radiology University of Pennsylvania United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania United States Biggs Alzheimer's Institute University of Texas San Antonio Health Science Center United States Laboratory of Behavioral Neuroscience National Institute on Aging United States Department of Neurology University of Pennsylvania United States iSTAGING consortium Baltimore Longitudinal Study of Aging United States Alzheimer’s Disease Neuroimaging Initiative United States Center for Biomedical Image Computing and Analytics University of Pennsylvania 3700 Hamilton Walk 7th Floor PhiladelphiaPA19104 United States

Heterogeneity of brain diseases is a challenge for precision diagnosis/prognosis. We describe and validate Smile-GAN (SeMI-supervised cLustEring-Generative Adversarial Network), a novel semi-supervised deep-clustering method, which dissects neuroanatomical heterogeneity, enabling identification of disease subtypes via their imaging signatures relative to controls. When applied to MRIs (2 studies;2,832 participants;8,146 scans) including cognitively normal individuals and those with cognitive impairment and dementia, Smile-GAN identified 4 neurodegenerative patterns/axes: P1, normal anatomy and highest cognitive performance;P2, mild/diffuse atrophy and more prominent executive dysfunction;P3, focal medial temporal atrophy and relatively greater memory impairment;P4, advanced neurodegeneration. Further application to longitudinal data revealed two distinct progression pathways: P1 → P2 → P4 and P1 → P3 → P4. Baseline expression of these patterns predicted the pathway and rate of future neurodegeneration. Pattern expression offered better yet complementary performance in predicting clinical progression, compared to amyloid/tau. These deep-learning derived biomarkers offer promise for precision diagnostics and targeted clinical trial recruitment. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Overcoming thresholds – Utilizing convolutional neural networks for predicting individual thrombectomy response in acute ischemic stroke

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Clinical Neurophysiology 2024年 159卷 e8-e8页

作者： von Braun, M. Scholl, K. Peter, L. Welle, F. Schneider, H.R. Wawrzyniak, M. Kaiser, D.P.O. Henkelmann, J. Prasse, G. Kellner, E. Reisert, M. Klingbeil, J. Stockert, A. Lobsien, D. Hoffmann, K. Scheuermann, G. Gilmann, C. Saur, D. Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI Dresden/Leipzig) Leipzig University Germany Neuroimaging Laboratory Department of Neurology University of Leipzig Medical Center Germany Institute of Neuroradiology University Medical Center Carl Gustav Carus Dresden Germany Department of Radiology University of Leipzig Medical Center Germany Institute of Neuroradiology University of Leipzig Medical Center Germany Medical Physics Department of Diagnostic and Interventional Radiology Medical Center – University of Freiburg Germany Institute for Diagnostic and Interventional Radiology and Neuroradiology Helios Hospital Erfurt Germany Image and Signal Processing Group Institute for Informatics Leipzig University Germany

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A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge

arXiv

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arXiv 2024年

作者： de la Rosa, Ezequiel Reyes, Mauricio Liew, Sook-Lei Hutton, Alexandre Wiest, Roland Kaesmacher, Johannes Hanning, Uta Hakim, Arsany Zubal, Richard Valenzuela, Waldo Robben, David Sima, Diana M. Anania, Vincenzo Brys, Arne Meakin, James A. Mickan, Anne Broocks, Gabriel Heitkamp, Christian Gao, Shengbo Liang, Kongming Zhang, Ziji Siddiquee, Md Mahfuzur Rahman Myronenko, Andriy Ashtari, Pooya Van Huffel, Sabine Jeong, Hyun-Su Yoon, Chi-Ho Kim, Chulhong Huo, Jiayu Ourselin, Sebastien Sparks, Rachel Clèrigues, Albert Oliver, Arnau Lladó, Xavier Chalcroft, Liam Pappas, Ioannis Bertels, Jeroen Heylen, Ewout Moreau, Juliette Hatami, Nima Frindel, Carole Qayyum, Abdul Mazher, Moona Puig, Domenec Lin, Shao-Chieh Juan, Chun-Jung Hu, Tianxi Boone, Lyndon Goubran, Maged Liu, Yi-Jui Wegener, Susanne Kofler, Florian Ezhov, Ivan Shit, Suprosanna Hernandez Petzsche, Moritz R. Menze, Bjoern Kirschke, Jan S. Wiestler, Benedikt Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Informatics Technical University Munich Germany Icometrix Leuven Belgium ARTORG Center for Biomedical Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Switzerland University of Bern Bern Switzerland Chan Division of Occupational Science and Occupational Therapy University of Southern California Los AngelesCA United States Stevens Neuroimaging and Informatics Institute Department of Neurology Keck School of Medicine University of Southern California United States University Institute of Diagnostic and Interventional Neuroradiology Inselspital Bern Switzerland University Institute of Diagnostic and Interventional Neuroradiology University Hospital Bern Inselspital University of Bern Bern Switzerland Department of Diagnostic and Interventional neuroradiology University Medical Center Hamburg-Eppendorf Hamburg Germany Department of Medical Imaging Radboud University Medical Center Institute for Health Sciences Nijmegen Netherlands Deepwise AI Lab Beijing China Beijing University of Posts and Telecommunications Bejing China School of Computing and Augmented Intelligence Arizona State University TempeAZ United States NVIDIA Santa ClaraCA United States STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Pohang Korea Republic of School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Institute of Computer Vision and Robotics University of Girona Spain Wellcome Centre for Human Neuroimaging University College London London United Kingdom Laboratory of Neuro Imaging Stevens Institute for Neuroimaging and Informatics Keck School of Medicine University of Southern California Los Angeles United States KU Leuven Leuven Belgium CREATIS Université Lyon1 CNRS UMR5220 INSERM U1206 INSA-Lyon Villeurbanne696

Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES’22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge segmentation algorithms by the research community. By assessing them against a hidden test set, we identified strengths, weaknesses, and potential biases. Through collaboration with leading teams, we combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions. Our ensemble model combines the individual algorithms’ strengths and achieved superior ischemic lesion detection and segmentation accuracy (median Dice score: 0.82, median lesion-wise F1 score: 0.86) on our internal test set compared to individual algorithms. This accuracy generalized well across diverse image and disease variables. Furthermore, the model excelled in extracting clinical biomarkers like lesion types and affected vascular territories. Notably, in a Turing-like test, neuroradiologists consistently preferred the algorithm’s segmentations over manual expert efforts, highlighting increased comprehensiveness and precision. Validation using a real-world external dataset (N=1686) confirmed the model’s generalizability (median Dice score: 0.82, median lesion-wise F1 score: 0.86). The algorithm’s outputs also demonstrated strong correlations with clinical scores (admission NIHSS and 90-day mRS) on par with or exceeding expert-derived results, underlining its clinical relevance. This study offers two key findings. First, we present an ensemble algorithm that detects and segments ischemic stroke lesions on DWI across diverse scenarios on par with expert (neuro)rad

关键词： Diagnosis

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