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This study introduced a novel framework that significantly improves the accuracy of alopecia areata lesion segmentation in semi-supervised scenarios, outperforming existing methods and aiding in the disease's diagnosis, treatment, and staging, which can impact quality of life and mental well-being.

This study found that their newly developed ADRL framework significantly improved the accuracy of scalp tissue layer segmentation in HR-MR images compared to existing methods.

This study found that self-supervised pretraining significantly improves accuracy in medical image classifiers for dermatology and chest X-ray tasks, outperforming supervised baselines and showing robustness to distribution shifts with limited labeled data.

This study introduced LabeledIn, a semi-automatic, human-reviewed catalog of drug-disease treatment relationships, highlighting the important role of human input in creating accurate and detailed drug indication resources.

This thesis presents research conducted in partial fulfillment of a Master's degree in Software Systems Engineering but does not report new empirical findings.