  High resolution micro-ultrasound has demonstrated promise in real-time
prostate cancer detection, with deep learning becoming a prominent tool for
learning complex tissue properties reflected on ultrasound. However, a
significant roadblock to real-world deployment remains, which prior works often
overlook: model performance suffers when applied to data from different
clinical centers due to variations in data distribution. This distribution
shift significantly impacts the model's robustness, posing major challenge to
clinical deployment. Domain adaptation and specifically its test-time adaption
(TTA) variant offer a promising solution to address this challenge. In a
setting designed to reflect real-world conditions, we compare existing methods
to state-of-the-art TTA approaches adopted for cancer detection, demonstrating
the lack of robustness to distribution shifts in the former. We then propose
Diverse Ensemble Entropy Minimization (DEnEM), questioning the effectiveness of
current TTA methods on ultrasound data. We show that these methods, although
outperforming baselines, are suboptimal due to relying on neural networks
output probabilities, which could be uncalibrated, or relying on data
augmentation, which is not straightforward to define on ultrasound data. Our
results show a significant improvement of $5\%$ to $7\%$ in AUROC over the
existing methods and $3\%$ to $5\%$ over TTA methods, demonstrating the
advantage of DEnEM in addressing distribution shift.
  \keywords{Ultrasound Imaging \and Prostate Cancer \and Computer-aided
Diagnosis \and Distribution Shift Robustness \and Test-time Adaptation.}
