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

Obed Korshie Dzikunu; Mohammad Mahdi Abootorabi; Mohamed Harmanani; Paul F. R. Wilson; Emma Willis; Ferdinand Luger; Adam Kinnaird; Brian Wodlinger

cs.CVAug 20, 2026

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

Authors: Obed Korshie Dzikunu, Mohammad Mahdi Abootorabi, Mohamed Harmanani, Paul F. R. Wilson, Emma Willis, Ferdinand Luger, Adam Kinnaird, Brian Wodlinger, +2 more

Organizations: Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada · Vector Institute, Toronto, Canada · School of Computing, Queen’s University, Kingston, Canada · Ordensklinikum Linz, Linz, Austria · Department of Surgery, University of Alberta, Edmonton, Canada · Exact Imaging, Markham, Canada

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.

Figures & tables

Figure 1: Overview of ANT framework. At inference, the first $n$ layers of the trained cancer detection encoder is domain-adapted via an auxiliary segmentation task guided by pseudo-masks from a frozen segmentation network. The adapted encoder is then used for cancer detection.

Center	Patients	Cores	Cancer %
A (source)	$693$	$5479$	$16\%$
B	$76$	$1118$	$34.6\%$
C	$42$	$569$	$27.0\%$

Table 1: Cohort statistics per clinical center. Centers differ in scanner generation and acquisition protocol, representing realistic deployment-time domain shift.

	Patient-Level csPCa AUC		Core-Level csPCa AUC		Core-Level PCa AUC
Method	B	C	B	C	B	C
No adaptation	$77.6(72.9-82.0)$	$79.4(66.8-89.9)$	$78.5(73.6-83.3)$	$84.5(74.0-93.1)$	$71.5(68.2-74.6)$	73.8 (68.5 – 79.3)
MEMO ( Zhang et al., 2022 )	$78.3(73.8-82.5)$	$83.5(73.0-91.7)$	$77.7(73.1-82.5)$	$83.4(71.8-92.9)$	$73.3(70.2-76.3)$	$70.6(65.3-76.3)$
TENT ( Wang et al., 2020 )	$76.0(71.6-80.2)$	$75.0(60.8-87.0)$	$80.1(76.0-84.3)$	$81.9(70.3-92.6)$	$74.5(71.5-77.3)$	$68.6(62.7-74.4)$
EATA ( Niu et al., 2022 )	$73.5(68.7-77.2)$	$69.4(65.1-72.8)$	$79.8(75.3-82.4)$	$80.5(77.3-84.2)$	74.9 (71.0 – 77.2)	$71.9(69.8-74.5)$
CoTTA ( Wang et al., 2022 )	$77.9(73.2-82.2)$	$77.9(64.6-89.7)$	$78.5(73.7-83.3)$	$84.1(73.8-92.7)$	$71.4(68.2-74.5)$	$72.9(67.4-78.4)$

Table 2: AUC (95% bootstrap CI) for all methods across both evaluation centres (B and C). Bold indicates the best performance per column.

	Sensitivity @ 60% Specificity (%)		Sensitivity @ 80% Specificity (%)
Method	B	C	B	C
No adaptation	$81.0(73.5-88.2)$	$85.3(43.7-100.0)$	$67.1(56.1-76.5)$	$70.1(43.7-94.1)$
MEMO ( Zhang et al., 2022 )	$82.5(75.3-89.4)$	$85.2(64.7-100.0)$	$61.7(51.3-72.1)$	$74.5(50.0-95.6)$
TENT ( Wang et al., 2020 )	$80.4(72.7-87.5)$	$83.1(47.1-100.0)$	$66.4(57.8-75.4)$	$71.1(47.1-91.7)$
EATA ( Niu et al., 2022 )	$80.2(71.3-86.4)$	$89.5(49.2-100.0)$	$65.5(57.3-73.2)$	$63.2(44.6-88.5)$
CoTTA ( Wang et al., 2022 )	$80.0(72.0-87.3)$	$83.4(53.3-100.0)$	$65.8(55.8-75.5)$	$75.2(53.3-94.4)$

Table 3: Sensitivity at fixed specificity (95% bootstrap CI) for all methods across both evaluation centres (B and C) at the core level for clinically significant prostate cancer (csPCa). Bold indicates the best performance per column.

Dice Tertile	$n$	Dice Median (Range)	ANT	Baseline	ANT $>$ Base	ANT	Baseline	ANT $>$ Base
			PCa AUC (%)			csPCa AUC (%)
Low $(\leq 0.72)$	11	$0.60(0.36-0.71)$	$80.0(29.6-100.0)$	$78.6(23.3-100.0)$	4/11	$100.0(78.1-100.0)$	$80.0(65.6-82.7)$	3/3
Mid $(0.72-0.90)$	10	$0.83(0.72-0.90)$	$68.3(6.2-100.0)$	$76.2(18.2-100.0)$	3/10	$63.6(50.0-95.8)$	$54.5(36.7-70.8)$	5/5
High $(>0.90)$	11	$0.95(0.91-0.99)$	$57.1(21.4-94.4)$	$44.0(21.4-96.3)$	6/11	$71.7(40.0-100.0)$	$65.7(23.1-100.0)$	4/6
Overall	32	$0.83(0.36-0.99)$	-	-	13/32	-	-	12/14

Table 4: Per-patient AUC for ANT and the unadapted baseline stratified by MicroSegNet pseudo-mask Dice score tertile. Values reported as median (range). csPCa AUC is available only for patients with at least one positive and one negative core for clinically significant cancer.

	Patient-Level csPCa AUC		Core-Level csPCa AUC		Core-Level PCa AUC
Method	Center B	Center C	Center B	Center C	Center B	Center C
PNF+ ( Wilson et al., 2026 )	$71.2(66.8-75.6)$	$75.6(64.4-85.3)$	$72.4(68.2-76.5)$	$75.4(62.8-87.2)$	$69.8(66.6-73.1)$	$63.8(58.1-69.4)$
PNF+ + ANT (ours)	72.8 (68.3 – 77.4)	80.5 (69.7 – 88.8)	73.9 (69.8 – 78.0)	78.2 (66.5 – 87.1)	∗ 73.1 (70.4 – 76.3)	65.2 (59.3 – 70.5)

Table 5: ANT applied to ProstNFound+ (PNF+), the current state-of-the-art model for micro-ultrasound prostate cancer detection. Results (AUC,

\%

) demonstrate model-independent generalization of the proposed TTA approach. Best results in bold.

	Sensitivity @ 60% Specificity (%)		Sensitivity @ 80% Specificity (%)
Method	B	C	B	C
PNF+ ( Wilson et al., 2026 )	$76.4(68.6-84.3)$	$72.3(50.0-92.9)$	$44.5(33.3-56.0)$	58.4 (30.4 – 87.5)
PNF+ + ANT (ours)	78.9 (70.6 – 86.4)	85.5 (65.0 – 100.0)	46.0 (36.2 – 56.7)	$56.0(30.8-77.8)$

Table 6: Sensitivity at fixed specificity (95% bootstrap CI) for the unadapted ProstNFound baseline (PNF) and ANT across both evaluation centres (B and C) at the core level for clinically significant prostate cancer (csPCa).

Figure 2: Left: AUROC for clinically significant cancer prediction evaluated across increasing involvement thresholds (cores with true involvement $\geq t$ or benign). Right: Distribution of activation leakage, defined as the ratio between average prostate heatmap activation and average needle-region activation, stratified by cancer status (benign vs PCa) and method, PNF+ (no TTA) vs. PNF+ + ANT. Lower values indicate better spatial specificity, i.e., reduced off-target activation outside the biopsy needle region.

Figure 3: Representative heatmaps of model activations without TTA (PNF+) and with TTA (PNF+ + ANT) on the same biopsy cores. Activations represent predicted involvement, with higher values indicating greater likelihood of clinically significant cancer. Each example is annotated with grade group (GG) and corresponding involvement percentage.

Layer position and parameter type
Update target	Position	Patient-Level (csPCa)	Core-Level (csPCa)	Core-Level (PCa)
Full blocks	First $n$	82.1	84.4	72.7
Full blocks	Middle $n$	$79.8$	$81.9$	73.6
Full blocks	Last $n$	$77.2$	$80.8$	$72.7$
Full blocks	All blocks	$74.9$	$79.6$	$70.2$
Norm only	First $n$	$76.4$	$80.9$	$72.8$

Table 7: Ablation on encoder update strategy and auxiliary task. Mean AUC (

\%

) across held-out test centers. Best in bold.

Inner steps $\tau$	Patient-Level (csPCa)	Core-Level (csPCa)	Core-Level (PCa)
0(w/o ANT)	$78.5$	$81.5$	$72.7$
2	$76.0$	$81.1$	$72.6$
5	$78.9$	$83.4$	$72.5$
10	82.1	84.4	72.7
20	$78.2$	$78.0$	$72.0$

Table 8: Ablation on number of TTA inner steps. Mean AUC (

\%

) across held-out test centers. Best in bold.

Appendix figures & tables4 assets

Supplementary material from the paper’s appendix.

Appendix

Hyperparameter	Value
TTA learning rate ( $\eta_{\mathrm{TTT}}$ )	$10^{-4}$
Encoder learning rate ( $\eta_{\mathrm{enc}}$ )	$10^{-5}$
Inner steps ( $\tau$ )	10
Blocks to adapt ( $n$ , DINOv3)	6
Blocks to adapt ( $n$ , MedSAM)	3
Segmentation head hidden dim	256

Appendix

Table 9: Selected TTA hyperparameters.

Method	Time (s)
No adaptation	$0.07\pm 0.01$
TENT	$0.11\pm 0.03$
EATA	$0.13\pm 0.01$
ROID	$0.19\pm 0.03$
SAR	$0.22\pm 0.02$
CoTTA	$0.26\pm 0.02$

Appendix

Table 10: Mean wall-clock inference time per image (seconds

\pm

std) on a single NVIDIA L40S GPU (48 GB). ANT includes

\tau=10

inner optimisation steps per image.

	Patient-Level csPCa AUC (%)		Core-Level csPCa AUC (%)		Core-Level PCa AUC (%)
Ordering	B	C	B	C	B	C
Fixed order	$79.9\pm 0.03$	$84.1\pm 0.08$	$81.1\pm 0.02$	$88.0\pm 0.06$	$73.1\pm 0.03$	$72.3\pm 0.03$
Random order	$80.0\pm 0.04$	$82.3\pm 0.03$	$80.6\pm 0.04$	$85.8\pm 0.08$	$72.9\pm 0.01$	$72.0\pm 0.05$

Appendix

Table 11: Ordering sensitivity analysis. Mean AUC (%)

\pm

std across five random seeds for fixed and randomly permuted test set orderings.

Subgroup (Involvement)	$n$	Baseline ECE (%)	ANT ECE (%)	$\Delta$ ECE (%)
Overall	1,118	7.6	2.7	$-4.9$
Low	161	29.7	32.0	$+2.2$
Mid	93	19.6	16.5	$-3.0$
High	131	14.3	13.8	$-0.5$

Appendix

Table 12: Calibration analysis on Center B (

n=1{,}118

cores). Expected Calibration Error (ECE) is reported for the unadapted baseline (PNF+) and ANT.

\Delta

ECE = ANT ECE

-

Baseline ECE; negative values indicate improved calibration.

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Medical Imaging Reinforcement Learning Medical Image Classification Ultrasound Imaging Weakly Supervised Learning Oncology+1

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Micro-ultrasound (

μ

US) is a new, emerging, and promising imaging modality for prostate cancer (PCa) detection, but accurate identification of suspicious tissue remains highly dependent on clinical experience, leading to substantial inter-observer variability. Machine-learning assistance can reduce this variability; however, training reliable deep models is challenging because supervision is sparse and noisy -- typically limited to core-level histopathology outcomes (e.g., cancer grade and its percentage in a biopsy core) without pixel-level lesion annotations and under severe class imbalance. We introduce Prost-RL, which reframes

μ

US PCa detection as a spatially aware, policy-driven inference problem by learning where to look before decoding. Prost-RL integrates a lightweight reinforcement-learning policy into a foundation-model encoder-decoder to generate interpretable spatial attention maps that act as soft prompts for both cancer-likelihood heatmap prediction and image-level classification. We further propose Adaptive Policy Optimization (APO) to stabilize hybrid supervised-RL training and a noise-robust objective combining symmetric cross-entropy with negative-entropy regularization to mitigate weak-label noise and encourage sharp localization. On a cohort of 6,607 biopsy cores from 693 patients across five clinical sites, Prost-RL achieves

79.0\pm3.5

AUROC with

64.6\pm6.3

% sensitivity at 80% specificity for core-level detection (+2.1 AUROC and +4.5 sensitivity points over the strongest baseline), and

79.3\pm5.8

AUROC for clinically significant cancer classification. The learned policy highlights biopsy-aligned regions, providing transparent, spatially grounded evidence alongside quantitative risk predictions. Code is available at: https://github.com/DeepRCL/Prost-RL.

Abstract

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