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LensePro: label noise-tolerant prototype-based network for improving cancer detection in prostate ultrasound with limited annotations

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

The standard of care for prostate cancer (PCa) diagnosis is the histopathological analysis of tissue samples obtained via transrectal ultrasound (TRUS) guided biopsy. Models built with deep neural networks (DNNs) hold the potential for direct PCa detection from TRUS, which allows targeted biopsy and subsequently enhances outcomes. Yet, there are ongoing challenges with training robust models, stemming from issues such as noisy labels, out-of-distribution (OOD) data, and limited labeled data.

Methods

This study presents LensePro, a unified method that not only excels in label efficiency but also demonstrates robustness against label noise and OOD data. LensePro comprises two key stages: first, self-supervised learning to extract high-quality feature representations from abundant unlabeled TRUS data and, second, label noise-tolerant prototype-based learning to classify the extracted features.

Results

Using data from 124 patients who underwent systematic prostate biopsy, LensePro achieves an AUROC, sensitivity, and specificity of 77.9%, 85.9%, and 57.5%, respectively, for detecting PCa in ultrasound. Our model shows it is effective for detecting OOD data in test time, critical for clinical deployment. Ablation studies demonstrate that each component of our method improves PCa detection by addressing one of the three challenges, reinforcing the benefits of a unified approach.

Conclusion

Through comprehensive experiments, LensePro demonstrates its state-of-the-art performance for TRUS-based PCa detection. Although further research is necessary to confirm its clinical applicability, LensePro marks a notable advancement in enhancing automated computer-aided systems for detecting prostate cancer in ultrasound.

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Notes

  1. Excessive hand motion is detected by checking the B-mode videos recorded during the biopsy procedure.

  2. We used a composition of random rotation (\(\pm 20\deg \)), contrast fluctuation, and crops.

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Acknowledgements

This research is supported by Natural Sciences and Engineering Research Council of Canada (NSERC) and the Canadian Institutes of Health Research (CIHR). Parvin Mousavi is supported by Canada CIFAR AI Chair and the Vector Institute. We acknowledge the staff at Vancouver General Hospital who assisted with data acquisition for our study.

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Author notes

  1. Fahimeh Fooladgar, Paul Wilson and Mohamed Harmanani have contributed equally to this work.

Authors and Affiliations

  1. Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada

    Minh Nguyen Nhat To, Fahimeh Fooladgar, Samira Sojoudi, Silvia Chang, Peter Black & Purang Abolmaesumi

  2. School of Computing, Queen’s University, Kingston, Canada

    Paul Wilson, Mohamed Harmanani, Mahdi Gilany, Amoon Jamzad & Parvin Mousavi

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  1. Minh Nguyen Nhat To
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  2. Fahimeh Fooladgar
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  3. Paul Wilson
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  5. Mahdi Gilany
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  8. Silvia Chang
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  9. Peter Black
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  10. Parvin Mousavi
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  11. Purang Abolmaesumi
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Corresponding authors

Correspondence to Minh Nguyen Nhat To, Parvin Mousavi or Purang Abolmaesumi.

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All studies involving human participants were in accordance with the ethical standards of the institutional research board and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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To, M.N.N., Fooladgar, F., Wilson, P. et al. LensePro: label noise-tolerant prototype-based network for improving cancer detection in prostate ultrasound with limited annotations. Int J CARS (2024). https://doi.org/10.1007/s11548-024-03104-3

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