Learning Prostate Anatomy at Test Time for Cancer Detection in Micro-Ultrasound

• URL: http://arxiv.org/abs/2608.20557v1
• Date: Thu, 20 Aug 2026 20:36:40 GMT
• Title: Learning Prostate Anatomy at Test Time for Cancer Detection in Micro-Ultrasound
• Abstract summary: Domain shift across clinical centers remains a fundamental barrier to deploying deep learning models for prostate cancer detection.<n>We propose ANT, a segmentation-guided TTA framework that adapts a pretrained cancer detection encoder to the target domain.<n>The model was trained on 693 patients imaged with an earlier-generation micro-ultrasound scanner in a multi-center clinical trial, and evaluated on 118 patients acquired with a newer-generation system across two centers.
• Score: 24.492164115951358
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Domain shift across clinical centers using different imaging hardware or acquisition protocols remains a fundamental barrier to deploying deep learning models for prostate cancer (PCa) detection. Existing test-time adaptation (TTA) methods address distribution shift through entropy minimization or augmentation-based self-supervision, correcting for statistical differences in image appearance but ignoring the anatomical structure of the target domain. We propose ANT, a segmentation-guided TTA framework that adapts a pretrained cancer detection encoder to the target domain by solving an auxiliary prostate segmentation task at test time, supervised by pseudo-masks from a frozen pretrained segmentation network. By aligning encoder representations to prostate anatomy in the target domain, ANT corrects domain-specific feature drift while preserving cancer-discriminative structure. The model was trained on 693 patients imaged with an earlier-generation micro-ultrasound scanner in a multi-center clinical trial, and evaluated on 118 patients acquired with a newer-generation system across two centers in another clinical trial. Under a leave-one-center-out protocol with identical evaluation conditions across all methods, ANT improves mean AUC by 2.9% and 3.6% at the biopsy-core and patient levels, respectively, over no adaptation, outperforming TTA baselines. Code is available at: https://github.com/ObedDzik/ant.git.

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