Thesis Defense: Learning Representations for Limited and Heterogeneous Medical Data

Speaker: Wei-Hung Weng , MIT CSAIL

Date: Tuesday, April 05, 2022

Time: 1:30 PM to 2:30 PM Note: all times are in the Eastern Time Zone

Public: Yes

Location: Seminar Room G449 (Patil/Kiva)

Event Type: Thesis Defense

Room Description:

Host: Peter Szolovits, MIT CSAIL

Contact: Wei-Hung Weng,

Relevant URL:

Speaker URL:

Speaker Photo:

Reminders to:,

Reminder Subject: TALK: Thesis Defense: Learning Representations for Limited and Heterogeneous Medical Data

For remote access to this event:

Thesis Advisors: Peter Szolovits
Thesis Committee: John Guttag, Leo Celi, Cameron Chen

Data insufficiency and heterogeneity are challenges of representation learning for machine learning in medicine due to the diversity of medical data and the expense of data collection and annotation. To learn generalizable representations from such limited and heterogeneous medical data, we aim to utilize various learning paradigms to overcome the issue. In the talk, we systematically explore the machine learning frameworks for limited data, data imbalance, and heterogeneous data, using cross-domain learning, self-supervised learning, and meta-learning. We present studies with different medical applications, such as clinical language translation, ultrasound image classification and segmentation, skin diagnosis classification. The investigation in this talk is not exhaustive but it introduces an extensive understanding of utilizing machine learning in helping clinical decision making under the limited and heterogeneous medical data setting.

Research Areas:
AI & Machine Learning

Impact Areas:
Health Care

This event is not part of a series.

Created by Wei-Hung Weng Email at Wednesday, March 30, 2022 at 9:06 AM.