About the Workshop
Digital pathology and molecular profiling now produce very large tissue images together with gene, protein, and clinical data that are heterogeneous, often incomplete, and hard to integrate. This workshop brings together researchers from machine learning, data mining, computational pathology, bioinformatics, and industry to develop scalable and interpretable methods that combine image, molecular, and clinical data for biomarker discovery, patient stratification, and translational research.
Pixels
Whole-slide, region-level, and cell-level tissue images
Molecules
Genomic, transcriptomic, and proteomic profiles
Clinical data
Outcomes, treatment, and patient-level variables
Biomarkers
Biomarker discovery, patient stratification, and translational research
Call for Papers
Topics of Interest
Topics include, but are not limited to:
- Computational pathology using whole-slide, region-level, and cell-level image data
- Multimodal learning across pathology images, omics data, and clinical variables
- Machine learning methods for integrating image, molecular, and clinical data
- Predicting molecular or biomarker signals from tissue morphology
- Weak supervision and learning from limited, noisy, or partially labeled biomedical data
- Learning with missing modalities or incomplete multimodal datasets
- Cross-modal representation learning, alignment, and fusion
- Foundation models for digital pathology and multimodal biomedical analysis
- Spatial modeling of tissue architecture and cell–cell interactions
- Scalable training and inference for large pathology image datasets
- Robust and generalizable models across cohorts, institutions, and assay platforms
- Benchmarking and evaluation for multimodal biomedical machine learning
- Applications in biomarker discovery, patient stratification, drug development, and translational research
Submission Guidelines
- Papers must be submitted as a PDF in the IEEE 2-column format, following the IEEE Computer Society Proceedings Manuscript Formatting Guidelines (templates).
- Full papers may be up to 10 pages and short papers up to 5 pages, including references, figures, and tables.
- Reviews are single-blind: please include author names and affiliations in the submission.
- Submissions must be original work that is not published or under review elsewhere.
- At least one author of each accepted paper must register for the conference and present the paper in person at the workshop in Phoenix, AZ, USA.
Proceedings and Indexing
All accepted workshop papers will be published by IEEE in the BigData 2026 Proceedings and will be submitted for inclusion in the IEEE Xplore Digital Library.
Submission Site
Papers are submitted through the IEEE BigData 2026 Cyberchair system. Please select this workshop when you submit. Paper submissions are due October 29, 2026 (11:59 PM AoE).
Important Dates
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Paper submission deadline
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Notification of acceptance
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Camera-ready papers due
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Workshop Exact workshop day to be announced · Phoenix, AZ, USA
All deadlines are 11:59 PM Anywhere on Earth (AoE).
Keynote Speakers
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Hanwen Xu
University of Washington
Keynote — title to be announced
Hanwen Xu is the lead author of GigaPath (Prov-GigaPath, Nature 2024), the whole-slide pathology foundation model that has been downloaded more than two million times and deployed in clinical digital pathology. His research at the University of Washington builds gigascale multimodal foundation models for precision medicine across pathology, radiology, and clinical records, including GigaTIME (Cell, 2025), LLaVA-Rad, and BiomedCLIP.
Organizers
Program Co-Chairs
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Zhongliang Zhou, Ph.D.
Senior Scientist, Merck & Co., Inc.
Zhongliang Zhou is a Senior Scientist at Merck working on computational pathology, multimodal modeling, and translational AI for biomarker-related applications. His interests include scalable digital pathology analysis, multimodal data integration, and AI-enabled biomarker discovery.
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Sheng Li, Ph.D.
Quantitative Foundation Associate Professor of Data Science, University of Virginia
Sheng Li is Quantitative Foundation Associate Professor of Data Science at the University of Virginia. His research focuses on AI, machine learning, and data-driven methods for biomedical, health, and life science applications.
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John Kang, Ph.D.
Senior Director, Merck & Co., Inc.
John Kang is Senior Director at Merck with expertise in multi-omics, bioinformatics, and biomarker analytics, including the integration of molecular profiling and imaging data for translational research.
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Wenhao Zhang
University of Virginia
Wenhao Zhang focuses on computational pathology and machine learning, with specific interests in multimodal foundation models and AI for precision medicine.
Program Committee
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Vladimir Svetnik, Ph.D. Distinguished Scientist, Merck & Co., Inc.
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Jie Zhou, Ph.D. Senior Scientist, Biometrics Research, Merck & Co., Inc. -
Brian Sang, Ph.D. Senior Scientist, Biometrics Research, Merck & Co., Inc. -
Qiaohui Zhou, Ph.D. Senior Scientist, Biometrics Research, Merck & Co., Inc. -
Zhuoyu Wen, Ph.D. Senior Scientist, Biogen -
Hanyin Wang, Ph.D. Senior Scientist, Biometrics Research, Merck & Co., Inc. -
Mohammad Yosofvand, Ph.D. Senior Scientist, Merck & Co., Inc.
Contact
For questions about the workshop, paper submissions, or participation, please contact the organizers at pixelsbiomarkers@gmail.com.