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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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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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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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Restoration of Marker Occluded Hematoxylin and Eosin Stained Whole Slide Histology images Using Generative Adversarial Networks

Restoration of Marker Occluded Hematoxylin and Eosin Stained...

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IEEE International Symposium on Biomedical Imaging

作者： Bairavi Venkatesh Tosha Shaht Antong Chen Soheil Ghafurian Image Data Analytics Informatics IT Merck & Co. Inc. West Point PA USA Image Data Analytics Informatics IT Merck & Co. Inc. Rahway NJ USA

ISBN: (数字)9781538693308

ISBN: (纸本)9781538693315

It is common for pathologists to annotate specific regions of the tissue, such as tumor, directly on the glass slide with markers. Although this practice was helpful prior to the advent of histology whole slide digitization, it often occludes important details which are increasingly relevant to immuno-oncology due to recent advancements in digital pathology imaging techniques. The current work uses a generative adversarial network with cycle loss to remove these annotations while still maintaining the underlying structure of the tissue by solving an image-to-image translation problem. We train our network on up to 300 whole slide images with marker inks and show that 70% of the corrected image patches are indistinguishable from originally uncontaminated image tissue to a human expert. This portion increases 97% when we replace the human expert with a deep residual network. We demonstrated the fidelity of the method to the original image by calculating the correlation between image gradient magnitudes. We observed a revival of up to 94,000 nuclei per slide in our dataset, the majority of which were located on tissue border.

关键词： Ink Training image reconstruction image segmentation Tumors Correlation Green products

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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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Restoration of marker occluded hematoxylin and eosin stained whole slide histology images using generative adversarial networks

arXiv

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arXiv 2019年

作者： Venkatesh, Bairavi Shah, Tosha Chen, Antong Ghafurian, Soheil Image Data Analytics Informatics IT Merck & Co. Inc. West PointPA19486 United States Image Data Analytics Informatics IT Merck & Co. Inc. RahwayNJ07065 United States

关键词： Restoration

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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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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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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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Author Correction: Enhancing the REMBRANDT MRI collection with expert segmentation labels and quantitative radiomic features

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Scientific data 2022年第1期9卷 386页

作者： Anousheh Sayah Camelia Bencheqroun Krithika Bhuvaneshwar Anas Belouali Spyridon Bakas Chiharu Sako Christos Davatzikos Adil Alaoui Subha Madhavan Yuriy Gusev Medstar Georgetown University Hospital Washington DC USA. Innovation Center for Biomedical Informatics (ICBI) Georgetown University Washington DC USA. Innovation Center for Biomedical Informatics (ICBI) Georgetown University Washington DC USA. kb472@georgetown.edu. Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA. Innovation Center for Biomedical Informatics (ICBI) Georgetown University Washington DC USA. yg63@georgetown.edu.

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