Prost-RL integrates an RL policy into a foundation-model encoder-decoder to generate interpretable spatial attention maps that improve core-level prostate cancer detection in micro-ultrasound, achieving 79.0 AUROC on a 6,607-core multi-site dataset.
IEEE Access11, 118217–118228 (2023)
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Learning Where to Look: A Reinforcement Learning Framework for Robust Micro-Ultrasound Prostate Cancer Detection
Prost-RL integrates an RL policy into a foundation-model encoder-decoder to generate interpretable spatial attention maps that improve core-level prostate cancer detection in micro-ultrasound, achieving 79.0 AUROC on a 6,607-core multi-site dataset.