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

2026-08-20

AI Summary

为解决不同临床中心间前列腺癌检测的域偏移问题，提出ANT方法，在测试时通过分割任务指导自适应，提高癌症检测精度。

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.

Problem

• domain shift
• prostate cancer detection
• test-time adaptation
• anatomical structure

Innovation

• Test-Time Adaptation (TTA)
• Prostate Segmentation
• Pseudo-Masks
• Domain Shift
• Cancer Detection