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Medical data spans multiple modals, each providing distinct insights into biological processes. For example, image data (e.g., MRI, CT, PET) captures structural and functional characteristics of tissues and organs, providing critical insights into anatomical variations, physiological processes, and disease progression. Omics data (e.g., connectomics, genomics) captures system-wide interactions within specific biological systems, enabling deeper insights into disease pathways and personalized medicine. Demographic and structured metadata enable population-level modeling and integration of heterogeneous biomedical data for understanding complex biological systems. AI-driven data mining is crucial for uncovering informative patterns and latent structures in multimodal medical data, enabling the integration and analysis of high-dimensional biomedical data to reveal complex associations and underlying mechanisms. However, applying AI to multimodal biomedical research faces significant challenges, including data heterogeneity, interpretability, precision, and the need for trustworthy and generalizable models. These challenges highlight the need for tailored AI solutions that ensure both robust performance and clinical trustworthiness. To address these challenges and explore how to effectively harness the benefits of advanced AI models for medical applications, we propose to organize a workshop that serves as a platform to foster interdisciplinary discussions, share advancements, and explore innovative AI-driven solutions for multimodal biomedical data analysis.

Topics

Overview: The rapid growth of multimodal biomedical data has created unprecedented opportunities for AI-driven data mining. Medical imaging (e.g., MRI, CT, PET), omics data (e.g., connectomics, genomics, proteomics), and structured metadata provide complementary insights into biological processes. AI methods play a critical role in integrating and analyzing these diverse data sources, enabling novel discoveries in disease mechanisms, precision medicine, and population health modeling. However, the complexity and heterogeneity of multimodal biomedical data introduce significant challenges. Issues such as data standardization, model interpretability, precision, and generalizability remain key barriers to effectively leveraging AI in biomedical research. This workshop aims to bring together researchers and practitioners to discuss recent advancements, emerging challenges, and future directions in AI-driven multimodal biomedical data mining.

Topics of Interest: We invite original contributions on topics including, but not limited to:

1. Deep learning for multimodal biomedical data analysis, including but not limited to medical imaging, genetic data, omics, and structured clinical metadata.
2. Large foundation models for healthcare applications leveraging multimodal medical data.
3. Multimodal fusion techniques for disease diagnosis, prognosis, and precision medicine.
4. Techniques for explainability, robustness, trustworthiness, and generalization in multimodal learning systems for medical applications.
5. Discovery of disease- and phenotype-associated biomarkers through multimodal large-scale data-driven approaches.
6. Computational challenges and advanced solutions in large-scale biomedical data mining.

Submissions

Authors are invited to submit original papers that have not been published elsewhere and are not currently under consideration for another journal, conference, or workshop. All submitted papers should follow the IEEE 2-column format.

• Long papers: Maximum 8 pages, including references and appendices.
• Short papers: Maximum 4 pages, including references and appendices.

All submissions will be triple-blind reviewed by the Program Committee based on technical quality, relevance to the scope of the conference, originality, significance, and clarity. Manuscripts must be submitted electronically through the online submission system: Submit your paper here . Email submissions are not accepted. Accepted papers will be presented at the workshop and published in the IEEE ICDM workshop proceedings.

Important Dates

• Paper submission deadline: August 21, 2025 (Tentative)
• Notification of acceptance: September 10, 2025 (Tentative)
• Camera-ready deadline: September 25, 2025 (Tentative)
• Workshop date: November 12, 2025

Organizers

Program Chairs

Haoteng Tang, PhD Assistant Professor University of Texas Rio Grande Valley

Lu Zhang, PhD Assistant Professor Indiana University Indianapolis

General Chairs

Paul M. Thompson, PhD Professor University of Southern California

Shuo Li, PhD Professor Case Western Reserve University

Danda B. Rawat, PhD Professor Howard University

Pingkun Yan, PhD Associate Professor Rensselaer Polytechnic Institute

Kewei Sha, PhD Associate Professor University of North Texas

Liang Zhan, PhD Associate Professor University of Pittsburgh

Web Chair

Guodong Liu, PhD Research Scientist Eli Lilly and Company

Local Chairs

Xiaowei Xu, PhD Associate Professor Guangdong Provincial People's Hospital

Di Ming, PhD Assistant Professor Chongqing University of Technology

Program Committee

Keynote Speakers

Heng Huang, Ph.D.

Brendan Iribe Endowed Professor

Department of Computer Science
University of Maryland, College Park

Talk Title: TBD

Dr. Heng Huang is the Brendan Iribe Endowed Professor in the Department of Computer Science at the University of Maryland, College Park. He also holds appointments in the Department of Electrical and Computer Engineering and the University of Maryland Institute for Advanced Computer Studies (UMIACS). Dr. Huang earned his Ph.D. in Computer Science from Dartmouth College and his M.S. and B.S. from Shanghai Jiao Tong University. His research interests encompass machine learning, data mining, big data computing, natural language processing, bioinformatics, neuroinformatics, precision medicine, health informatics, computer vision, and medical image analysis.

Lifang He, Ph.D.

Associate Professor

Department of Computer Science and Engineering
Lehigh University

Talk Title: TBD

Dr. Lifang He is an Associate Professor in the Department of Computer Science and Engineering at Lehigh University. She received her B.S. degree in Computational Mathematics from Northwest Normal University and her Ph.D. in Computer Science from South China University of Technology. Prior to joining Lehigh University, Dr. He was a postdoctoral associate at the University of Pennsylvania's Perelman School of Medicine and Weill Cornell Medical College. Her research interests include machine learning, deep learning, data mining, tensor analysis, and biomedical informatics.

Agenda

TBD

Accepted Papers

TBD

Photos

TBD

Contact

If you have any questions regarding the workshop, feel free to reach out to us:

• Email: haoteng.tang@utrgv.edu (Dr. Haoteng Tang)
• Email: lz50@iu.edu (Dr. Lu Zhang)