AI for Precision Medicine: Integrative Analysis of Histopathology Images and Spatial Omics
The IEEE International Conference on Data Mining (ICDM) Tutorial 2025
November 14th, 2025
Washington DC, USA
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Abstract
Hematoxylin and eosin (H&E) imaging is the gold standard in clinical pathology, providing detailed visualization of tissue and cellular morphology. Spatial omics complement H&E images by offering spatially resolved molecular profiles. Together, these modalities are transforming our ability to interrogate tissues by integrating molecular resolution with structural context.
In this tutorial, we present recent advances in spatial omics technologies combined with H&E histopathology. Methodologically, we trace the shift from statistical deconvolution to deep learning, highlighting graph neural networks (GNNs), transformers, and encoder-based architectures for cross-modal alignment, microenvironment modeling, etc. Downstream tasks such as cell deconvolution, spatial domain identification, spatial reconstruction, and gene imputation are systematically reviewed with showcase applications. We conclude with open challenges in interpretability and point toward multi-omics spatial assays and scalable multimodal frameworks. This tutorial equips the audience with a unified view of integrating spatial biology and pathology at the cellular level through advanced machine learning methods.
Materials
Tutorial slides:
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Survey paper:
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Schedule
Time: November 14th, 2025, 10-11:50am
Location: Washington DC, USA
Outline
Introduction to H&E Images and Spatial Omics
Spatial Data Modalities: H&E Images and Omics Data
Current State of Art and Motivation
Machine Learning Methods: From Statistics to Deep Learning
Downstream Tasks of Spatial Omics with a Showcase
Open Challenges and Future Directions
Summary, Q&A and Interactive Discussion
Tutors' Bio
Ninghui Hao is currently a Ph.D. student in Computer Science at the University of Texas at Arlington. Her research focuses on AI for Science (AI4Science), with interests in bioinformatics, explainable machine learning, multimodal learning, and interpretable models for clinical decision-making. Her work has been published in venues including the Association for the Advancement of Artificial Intelligence (AAAI), the American Association for Cancer Research (AACR) and others.
Boshen Yan is a Ph.D. student in Computational Biology at Carnegie Mellon University. He completed his master’s degree in Biomedical Informatics at Harvard Medical School and his bachelor’s degree in Computational Biology at the National University of Singapore. His research interests include the development of multimodal integration methods to analyze genetic diseases.
Dong Li is currently a Ph.D. student in the Department of Computer Science at Baylor University. His main research directions include graph mining, fairness-aware machine learning, domain generalization, and computational biology. He has received multiple academic scholarships and national competition awards. His publications have been accepted by top international conferences such as KDD, IJCAI, CIKM, etc.
Chen Zhao is an Assistant Professor in the Department of Computer Science at Baylor University. His research focuses on machine learning, data mining, and computational biology, particularly trustworthy machine learning, novelty detection, and domain generalization. His publications have been accepted and published in premier conferences, including KDD, CVPR, IJCAI, ICDE, AAAI, WWW, etc. Dr. Zhao served as a PC member of top international conferences, such as KDD, NeurIPS, IJCAI, ICML, AAAI, ICLR, etc. He has organized and chaired multiple workshops on topics of Ethical AI, Uncertainty Quantification, Distribution Shifts, and Trustworthy AI for Healthcare at KDD (2022, 2023, 2024, 2025), AAAI (2023), IEEE BigData (2024), and SDM (2025). He serves as the chair of the Challenge Cup of the IEEE Bigdata 2024 conference, the tutorial chair for the PAKDD 2025 and ICDM 2025 conferences, and the workshop chair for the IEEE Bigdata 2025 conference.
Guihong Wan is an Instructor of Dermatology at Massachusetts General Hospital and Harvard Medical School. Her research focuses on developing computational methodologies for integrative analyses of biomedical data and building biologically explainable machine learning models for predicting patient outcomes. Her research has been published in AAAI, IJCAI, ICDE, The Lancet Oncology, npj Precision Oncology, Briefings in Bioinformatics, Journal of the American Academy of Dermatology, British Journal of Dermatology, Nature Medicine, and others. She served as the tutorial co-chair for the 16th International Conference on Brain Informatics, 2023.
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