Topic: AI-based Image Analysis for Medical
Problems: Challenges and New Approaches
Speaker: Prof. Danny Z. Chen Department of
Computer Science and Engineering University of Notre Dame
Time : April 22, 2026 (Wednesday), 12:10-14:00
Venue: The Management Building, 11F, AI Lecture Hall
Join Online: https://reurl.cc/epD4WW or scan QR code on poster
Registration link: https://forms.gle/XiK5ecK3LCjuzydJ6 or scan QR code on poster
About the Speaker:
Dr. Danny Z. Chen received the B.S. degrees in Computer Science and in Mathematics from the University of San Francisco, California in 1985, and the M.S. and Ph.D. degrees in Computer Science from Purdue University, West Lafayette in 1988 and 1992, respectively. He is a Professor in the Department of Computer Science and Engineering at the University of Notre Dame. Dr. Chen’s main research interests include computational biomedicine, biomedical imaging, machine learning, data mining, computational geometry, algorithms, and VLSI. He has worked extensively with biomedical researchers and practitioners, published many papers in these areas, and holds 8 US patents for technology development in biomedical applications. He received the US NSF CAREER Award in 1996 and the 2017 PNAS Cozzarelli Prize of the US National Academy of Sciences. He is a Fellow of IEEE and AAAS, and is a Distinguished Scientist of ACM.
Abstract:
New technologies for acquiring massive
amounts of medical image data give rise to an ever-increasing demand for
effective approaches for medical image analysis tasks. In recent years, deep
learning (DL) approaches have yielded remarkably high-quality solutions for
numerous medical imaging applications, largely outperforming traditional image
analysis methods. Comparing to natural scene images, medical image analysis
faces several different challenges. Commonly, DL methods rely on a large amount
of labeled data for model training. While natural scene images are usually 2D,
medical images can be 2D, 3D, and even higher dimensional. In particular, 3D
medical images are widely used in basic research and clinical practice.
However, 3D medical image analysis presents big challenges to DL methods.
First, 3D medical images can be of very large sizes (e.g., billions of voxels),
and thus incur high computational costs. But, current GPUs are of limited
memory for implementing 3D DL models. Furthermore, few efficient automatic
techniques for labeling 3D images are available. Since in general, only trained
medical experts can label medical images effectively, medical image annotation
is a highly costly and labor-intensive process (even for 2D images). Therefore, how to attain sufficient good
quality labeled image data for DL model training while significantly reducing
annotation efforts of medical experts is a big bottleneck to the successful
development and deployment of DL methods for medical imaging applications.
We present new DL-based approaches for
medical image analysis tasks (segmentation, classification, denoising, etc). We
show that it is often not enough to simply apply DL methods alone to tackle
medical image analysis problems. Thus, our approaches are based on combinations
of DL methods and algorithmic techniques (e.g., topological data analysis). For
example, our sparse annotation schemes judiciously select the most
representative or valuable samples to label. Actually, the problem of finding
an optimal subset of samples (as sparse labeled data) to cover or represent an
entire image dataset is an NP-hard problem, which can be solved approximately
with guaranteed good quality. Our approaches achieve high performances with
efficient costs. We present experimental results on various datasets to
demonstrate the applicability of our approaches on medical image analysis
problems.
Organizers: Institute of Health Data
Science