Date of Award
Summer 8-4-2026
Document Type
Thesis
Publication Status
Version of Record
Submission Date
August 2026
Department
Computer and Electrical Engineering and Computer Science
College Granting Degree
College of Engineering and Computer Science
Department Granting Degree
Electrical Engineering and Computer Science
Degree Name
Master of Science (MS)
Thesis/Dissertation Advisor [Chair]
Ionut Cardei
Abstract
Weakly supervised medical imaging models trained with coarse image-level labels often report strong performance on metrics such as accuracy and AUC. This thesis argues that these metrics can be misleading: a model may succeed on a coarse diagnostic task while failing to preserve the fine-grained information needed for consequential clinical decisions. It makes this failure measurable through the diagnostic gap, defined as the divergence between coarse and fine-grained diagnostic preservation, across three connected studies. The first shows that near-perfect ovarian ultrasound accuracy reflects visual separability rather than pathological understanding. The second measures the diagnostic gap in a mammographic pipeline, where coarse lesion-type features are preserved while malignancy information is lost. The third explains the mechanism: only about 4.4% of latent magnitude aligns with supervisory gradients. Before clinical deployment, models must therefore be evaluated at the appropriate level of diagnostic granularity, not just through simple accuracy.
Recommended Citation
Bertrand, Vinceline, "EVALUATING DIAGNOSTIC INFORMATION PRESERVATION IN WEAKLY SUPERVISED MEDICAL IMAGING" (2026). Electronic Theses and Dissertations. 357.
https://digitalcommons.fau.edu/etd_general/357