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SUMMARY:Carrión\, H. (CSE) - Deep Learning Algorithms for Medical Image Representation Learning and Understanding
DESCRIPTION:AI-assisted clinical decisions in medicine\, and particularly in dermatology\, demand fine-grained understanding across diverse skin tones\, body sites\, and disease types\, yet expert-annotated datasets are scarce\, demographically imbalanced\, and almost devoid of rare presentations. This dissertation develops four deep learning systems for this low-label\, low-coverage regime. We introduce HealNet\, which learns wound healing stages from longitudinal photographs without any human labels\, reaching 90.6% downstream stage-classification accuracy on a small longitudinal cohort. The Fair\, Efficient\, and Diverse Diffusion (FEDD) model then leverages powerful diffusion-model embeddings to build a skin-tone-fair\, data-efficient classifier for skin lesions\, matching or exceeding state-of-the-art performance while using only 5-20% of available labels and contributing explicit skin-tone-stratified fairness evaluation of the work. Next\, Controllable Generation of Diverse Dermatological Imagery (cgDDI) re-tasks this diffusion model to controllably synthesize skin-tone-balanced dermatological imagery\, growing a small biopsy-confirmed dataset by over 400x and reaching state-of-the-art 90.9% accuracy and improved fairness in malignancy classification\, with a +13.9% cross-dataset gain on the Fitzpatrick17k benchmark. Finally\, we introduce D-Synth and DermDepth: a synthetic dermoscopic dataset with pixel-perfect 3D ground truth and a metric-scale foundation model that closes the loop into 3D dermatology\, correcting metric scale error from over 16x to under 1.1x on real dermoscopic data and enabling single-photograph measurement of lesion reconstruction: size\, area\, and volume without specialized hardware. All data\, code\, and models are released openly to support reproducibility and ongoing fairness research. \nEvent Host:  Héctor Carrión\, Ph.D. Candidate\, Computer Science & Engineering \nAdvisor: Narges Norouzi \nZoom: https://ucsc.zoom.us/j/96678782408?pwd=71f0ObEnUMNgkZ9NYnpbFLMlg1Pdm0.1 \nPasscode: 0FMVtz
URL:https://events.ucsc.edu/event/carrion-h-cse-deep-learning-algorithms-for-medical-image-representation-learning-and-understanding/
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CATEGORIES:Ph.D. Presentations
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