Language-Centric Medical Image Understanding
Name
wang-wpq-phd-eecs-2025-thesis.pdf
Description
Thesis PDF
Size
24.01 MB
Format
Adobe PDF
Checksum (MD5)
e38a32cfb965bb3fad0421730b0caeca
Author(s)
Wang, Peiqi
Advisor(s)
Golland, Polina
Date Issued
May 2025
Publisher
Massachusetts Institute of Technology
Abstract
This thesis advances medical image understanding by leveraging the multifaceted roles of language: as supervision, prior knowledge, and a medium for communication. We introduce three main contributions: (1) a weakly supervised framework that uses language in clinical reports to guide fine-grained alignment between image regions and textual descriptions, (2) an adaptive debiasing method that uses language prior to improve the robustness of learning algorithms under noisy supervision, and (3) a novel approach for calibrating linguistic expressions of diagnostic certainty, enabling more reliable communication of clinical findings. Together, these methods lead to more accurate, robust, and reliable machine learning systems, ultimately streamlining clinical workflows and improving patient care.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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