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.
Computerized Medical Imaging and Graphics112, 102326 (2024)
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A multi-view transformer framework integrating rotational micro-ultrasound sweeps with biopsy frames achieves 87.2% patient-level AUROC for prostate cancer detection, outperforming single-frame and video baselines.
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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.
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Compass: Prostate Cancer Detection Needs Multi-View Context
A multi-view transformer framework integrating rotational micro-ultrasound sweeps with biopsy frames achieves 87.2% patient-level AUROC for prostate cancer detection, outperforming single-frame and video baselines.