THESIS DEFENSE: Effective Modeling in Medical Imaging with Constrained Data
Host
Peter Szolovits
[ZOOM]
https://mit.zoom.us/j/5323046147
[TITLE]
Effective Modeling in Medical Imaging with Constrained Data
[SPEAKER]
Tzu Ming Harry Hsu
[ROOM]
32-D463 (Star)
[TIME]
Aug 15, 15:00 - 16:00
[ABSTRACT]
Data for modern medical imaging modeling is constrained by their high physical density, complex structure, insufficient annotation, heterogeneity across sites, long-tailed distribution, and sparsely presented information. In this talk, I will cover three of my research to combat these scenarios: the first work focuses on using surrogate medical endpoint to quantify cancer mortality via automated body composition assessment; the second work utilizes deep reinforcement learning to learn better from weakly supervised dental imaging data for a finding summarization; lastly, the third work investigates federated learning settings on how they benefit cross-site collaborative learning and how non-identical and imbalanced data can impact the learning quality in real-world settings. The objective of this talk is to provide a coverage of various methods that more effectively model medical imaging tasks when the available data are constrained.
https://mit.zoom.us/j/5323046147
[TITLE]
Effective Modeling in Medical Imaging with Constrained Data
[SPEAKER]
Tzu Ming Harry Hsu
[ROOM]
32-D463 (Star)
[TIME]
Aug 15, 15:00 - 16:00
[ABSTRACT]
Data for modern medical imaging modeling is constrained by their high physical density, complex structure, insufficient annotation, heterogeneity across sites, long-tailed distribution, and sparsely presented information. In this talk, I will cover three of my research to combat these scenarios: the first work focuses on using surrogate medical endpoint to quantify cancer mortality via automated body composition assessment; the second work utilizes deep reinforcement learning to learn better from weakly supervised dental imaging data for a finding summarization; lastly, the third work investigates federated learning settings on how they benefit cross-site collaborative learning and how non-identical and imbalanced data can impact the learning quality in real-world settings. The objective of this talk is to provide a coverage of various methods that more effectively model medical imaging tasks when the available data are constrained.