Al-Driven Multimodal Medical Imaging: Innovations for Early Diagnosis and Precision Care
Contents
Chair
Yudong Zhang
School of Computing and Mathematical Sciences, University of Leicester, Leicester, UK.
A leading expert in the field of artificial intelligence, deep learning, and medical image processing.
A leading expert in the field of artificial intelligence, deep learning, and medical image processing.
Professor Yudong Zhang is a professor and doctoral supervisor at the University of Leicester, UK. His research interests include artificial intelligence, deep learning, and medical image processing. From 2014 to 2018, he was listed as a Highly Cited Researcher in China (Computer Science) by Elsevier. He was recognized as a Clarivate Highly Cited Researcher globally in 2019, 2021, and 2022. Additionally, from 2020 to 2022, he was ranked among the top 2% of scientists worldwide by Stanford University.
Speaker(s)
Lalithkumar Seenivasan
Advanced Robotics and Computationally AugmenteD Environments (ARCADE) Lab, Johns Hopkins University, Baltimore, Maryland, USA.
Liyue Shen
Electrical Engineering and Computer Science, College of Engineering, University of Michigan, Ann Arbor, MI, USA.
Programme
Programme
| Time (UTC+8) | Chair/Speakers | Topic |
| 8:00-8:05 PM | Chair (Prof. Yudong Zhang) | Welcome Remarks & and Introduction to Speakers |
| 8:05-8:10 PM | Editorial Office | Journal Introduction |
| 8:10-8:25 PM | Dr. Lalithkumar Seenivasan | Advancing Automation in Healthcare Through Multimodal Reasoning |
| 8:25-8:40 PM | Prof. Liyue Shen | Dynamic Modelling of Patients, Modalities and Tasks via Multi-modal Multi-task Mixture of Experts |
| 8:40-8:55 PM | All | Disscussion (Q&A) |
| 8:55-9:00 PM | Chair | Closing Remarks |
Relevant Special Topic(s)
Topic: AI-powered Medical Imaging and Diagnosis
https://www.oaepublish.com/specials/ir.10052Moments
Presentation
Dr. Lalithkumar Seenivasan:
Advancing Automation in Healthcare Through Multimodal Reasoning
NaN
Prof. Liyue Shen:
Dynamic Modelling of Patients, Modalities and Tasks via Multi-modal Multi-task Mixture of Experts
NaN
