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Jiaqi Wang[1], Xiaochen Wang[1], Yuan Zhong[1], Ziyi Yin[1], Aofei Chang[1], Cao Xiao[2], Fenglong Ma[1]
[1] The Pennsylvania State University [2] GE Healthcare
AAAI 2025
Time: 2:00 PM - 6:00 PM (EST), Feb 26, 2025
Time: 2:00 PM - 6:00 PM (EST), Feb 26, 2025
Location: Room: Room 115C, Philadelphia Convention Center, Philadelphia, PA USA
Slides: https://drive.google.com/file/d/12mWUCqdcicqHagu4nCeR1YGFz-vVjlcr/view?usp=sharing
Description:
Description:
Multimodal learning has demonstrated its potential to offer deeper insights from data that spans various modalities. In the healthcare domain, designing multimodal AI systems to better understand multimodal healthcare data—thereby providing more comprehensive medical support—remains an emerging and relatively unexplored area. This tutorial will focus on exploring multimodal healthcare data, presenting recent advances in multimodal data fusion, pertaining, generation, and privacy-preserving techniques in healthcare, and finally discussing open challenges and future research directions in this rapidly evolving field. This tutorial welcomes researchers, healthcare professionals, students, engineers, and practitioners. The tutorial is designed to balance introductory and advanced material, with 50% geared toward beginners and 50% aimed at intermediate and advanced learners. By the end, attendees will gain a comprehensive understanding of multimodal AI, multimodal health informatics, cutting-edge research, and emerging research directions in this field.
Outline:
Outline:
Part 1: Multimodal AI in Healthcare (15 min)
Part 2: Multimodal Pre-training for Healthcare (60 min)
Preliminaries: Multimodal Pre-training
Medical Contrastive Pre-training
Medical Generative Pre-training
Part 3: Multimodal Health Data Generation (60 min + 30 min break)
GAN-based Models
VAE-based Models
Transformer-based Models
Diffusion-based Models
Part 4: Privacy-preserving Multimodal Healthcare Mining (60 min)
Preliminaries: Federated Learning
Multimodal Federated Learning for Healthcare
Federated Medical Foundation Models
Part 5: Conclusion, Future Work, and Q&A (15 min)
Presenters:
Presenters:
Xiaochen Wang is a Ph.D. candidate in the College of Information Sciences and Technology at The Pennsylvania State University. His research interests span healthcare informatics, multimodal learning, and information retrieval. He earned his Bachelor's degree from Central South University in China and a Master's degree from the University of North Carolina at Chapel Hill. His recent work focuses on advancing multimodal healthcare foundation models using innovative techniques such as federated learning and parameter-efficient fine-tuning.
Xiaochen Wang is a Ph.D. candidate in the College of Information Sciences and Technology at The Pennsylvania State University. His research interests span healthcare informatics, multimodal learning, and information retrieval. He earned his Bachelor's degree from Central South University in China and a Master's degree from the University of North Carolina at Chapel Hill. His recent work focuses on advancing multimodal healthcare foundation models using innovative techniques such as federated learning and parameter-efficient fine-tuning.
Yuan Zhong is currently pursuing his Ph.D. in the College of Information Sciences and Technology at The Pennsylvania State University, Yuan Zhong conducts research at the intersection of diffusion models, healthcare data generation, and multimodal learning. He has contributed to major academic conferences, with publications appearing in KDD, SDM, EMNLP, IJCAI, and etc. Prior to his doctoral studies, he completed his M.S. at Boston University and earned his B.S. from the University of Illinois at Urbana-Champaign. His complete publication record can be found on his Google Scholar profile: https://scholar.google.com/citations?hl=en&user=58wkbnMAAAAJ.
Yuan Zhong is currently pursuing his Ph.D. in the College of Information Sciences and Technology at The Pennsylvania State University, Yuan Zhong conducts research at the intersection of diffusion models, healthcare data generation, and multimodal learning. He has contributed to major academic conferences, with publications appearing in KDD, SDM, EMNLP, IJCAI, and etc. Prior to his doctoral studies, he completed his M.S. at Boston University and earned his B.S. from the University of Illinois at Urbana-Champaign. His complete publication record can be found on his Google Scholar profile: https://scholar.google.com/citations?hl=en&user=58wkbnMAAAAJ.
Jiaqi Wang is currently a Ph.D. from the PSU Data Science Lab in the College of Information Sciences and Technology at the Pennsylvania State University. He received his B.E. and M.S. from Zhejiang University and the University of Georgia. His research interests are federated learning, healthcare informatics, and multimodal medical foundation models. He has also served as a reviewer or committee member in major machine learning and data mining conferences. More information can be found via the link: https://jackqqwang.github.io.
Jiaqi Wang is currently a Ph.D. from the PSU Data Science Lab in the College of Information Sciences and Technology at the Pennsylvania State University. He received his B.E. and M.S. from Zhejiang University and the University of Georgia. His research interests are federated learning, healthcare informatics, and multimodal medical foundation models. He has also served as a reviewer or committee member in major machine learning and data mining conferences. More information can be found via the link: https://jackqqwang.github.io.
Dr. Fenglong Ma is currently an Assistant Professor in the College of Information Science and Technology at the Pennsylvania State University (PSU), leading the PSU Data Science Lab. He received his Ph.D. from the Department of Computer Science and Engineering, University at Buffalo (UB) in 2019, and subsequently joined PSU. His research interests lie in data mining and machine learning, with an emphasis on mining health-related data. His research interests also include federated learning, multimodal learning, health informatics, natural language processing, and security. He has publications in top conferences and journals such as KDD, NeurIPS, WWW, AAAI, IJCAI, ACL, EMNLP, CIKM, WSDM, ICDM, SDM, and TKDE. He was honored to be recognized as the awardee of the NSF CAREER Award, Sony Research Award, and UB CSE 2019 Best Ph.D. Dissertation Award. He was also recognized as AI 2000 Most Influential Scholar Honorable Mention in Data Mining (2022 and 2023) and 2022 Global Top 50 Chinese Rising Stars in Data Mining. More information can be found at his website: https://fenglong-ma.github.io.
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