[Thesis Defense] Language-Centric Medical Image Understanding

This thesis improves how machines understand medical images by using language in three ways: as a source of supervision, as prior knowledge, and as a way to communicate results. The main contributions are:  (1) a method that uses the text in medical reports to help link specific parts of an image to the descriptions, (2) a way to make learning from noisy and imbalanced clinical data more robust by using language-based cues to correct bias on a case-by-case basis, and (3) a framework for assessing and improving how well linguistic expressions of diagnostic certainty are calibrated. Together, these contributions improve the accuracy, robustness, and reliability of medical AI systems, supporting more effective clinical workflows and improved patient care.