Abstract
Purpose
While deep learning methods have shown great promise in improving the effectiveness of prostate cancer (PCa) diagnosis by detecting suspicious lesions from trans-rectal ultrasound (TRUS), they must overcome multiple simultaneous challenges. There is high heterogeneity in tissue appearance, significant class imbalance in favor of benign examples, and scarcity in the number and quality of ground truth annotations available to train models. Failure to address even a single one of these problems can result in unacceptable clinical outcomes.
Methods
We propose TRUSWorthy, a carefully designed, tuned, and integrated system for reliable PCa detection. Our pipeline integrates self-supervised learning, multiple-instance learning aggregation using transformers, random-undersampled boosting and ensembling: These address label scarcity, weak labels, class imbalance, and overconfidence, respectively. We train and rigorously evaluate our method using a large, multi-center dataset of micro-ultrasound data.
Results
Our method outperforms previous state-of-the-art deep learning methods in terms of accuracy and uncertainty calibration, with AUROC and balanced accuracy scores of 79.9% and 71.5%, respectively. On the top 20% of predictions with the highest confidence, we can achieve a balanced accuracy of up to 91%.
Conclusion
The success of TRUSWorthy demonstrates the potential of integrated deep learning solutions to meet clinical needs in a highly challenging deployment setting, and is a significant step toward creating a trustworthy system for computer-assisted PCa diagnosis.
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These first two components follow the methodology of our previous work [14].
We used 1 convolution per residual block instead of 2.
We found those parameters to be the best. We tested depths of 4, 6, 8, and inner dimensions of 128, 256.
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Natural Sciences and Engineering Research Council of Canada (NSERC), Canadian Institutes of Health Research, Canadian Institute for Advanced Research, Vector Institute.
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This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC), and the Canadian Institutes of Health Research (CIHR). Parvin Mousavi is supported by the CIFAR AI Chair and the Vector Institute. Brian Wodlinger is Vice President of Clinical and Engineering at Exact Imaging. None of the other authors have potential Conflict of interest to disclose. All patient data were used with informed consent and approval of institutional ethics boards.
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Harmanani, M., Wilson, P.F.R., To, M.N.N. et al. TRUSWorthy: toward clinically applicable deep learning for confident detection of prostate cancer in micro-ultrasound. Int J CARS 20, 981–989 (2025). https://doi.org/10.1007/s11548-025-03335-y
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DOI: https://doi.org/10.1007/s11548-025-03335-y