Abstract
MOTIVATION: Detection of prostate cancer during transrectal ultrasound-guided biopsy is challenging. The highly heterogeneous appearance of cancer, presence of ultrasound artefacts, and noise all contribute to these difficulties. Recent advancements in high-frequency ultrasound imaging - micro-ultrasound - have drastically increased the capability of tissue imaging at high resolution. Our aim is to investigate the development of a robust deep learning model specifically for micro-ultrasound-guided prostate cancer biopsy. For the model to be clinically adopted, a key challenge is to design a solution that can confidently identify the cancer, while learning from coarse histopathology measurements of biopsy samples that introduce weak labels. METHODS: We use a dataset of micro-ultrasound images acquired from 194 patients, who underwent prostate biopsy. We train a deep model using a co-teaching paradigm to handle noise in labels, together with an evidential deep learning method for uncertainty estimation. We evaluate the performance of our model using the clinically relevant metric of accuracy vs. confidence. RESULTS: Our model achieves a well-calibrated estimation of predictive uncertainty with area under the curve of 88\(\%\). The use of co-teaching and evidential deep learning in combination yields significantly better uncertainty estimation than either alone. We also provide a detailed comparison against state-of-the-art in uncertainty estimation.
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References
Abdar, M., et al.: A review of uncertainty quantification in deep learning: techniques, applications and challenges. Inf. Fusion 76, 243–297 (2021)
Abouassaly, R., Klein, E.A., El-Shefai, A., Stephenson, A.: Impact of using 29 mhz high-resolution micro-ultrasound in real-time targeting of transrectal prostate biopsies: initial experience. World J. Urol. 38(5), 1201–1206 (2020)
Ahmed, H.U., et al.: Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. Lancet 389(10071), 815–822 (2017)
Eure, G., Fanney, D., Lin, J., Wodlinger, B., Ghai, S.: Comparison of conventional transrectal ultrasound, magnetic resonance imaging, and micro-ultrasound for visualizing prostate cancer in an active surveillance population: a feasibility study. Can. Urol. Assoc. J. 13(3), E70 (2019)
Gal, Y., Ghahramani, Z.: Dropout as a bayesian approximation: representing model uncertainty in deep learning. In: International Conference on Machine Learning, pp. 1050–1059. PMLR (2016)
Ghai, S., et al.: Assessing cancer risk on novel 29 mhz micro-ultrasound images of the prostate: creation of the micro-ultrasound protocol for prostate risk identification. J. Urol. 196(2), 562–569 (2016)
Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: International Conference on Machine Learning, pp. 1321–1330. PMLR (2017)
Han, B., et al.: A survey of label-noise representation learning: Past, present and future. arXiv preprint arXiv:2011.04406 (2020)
Han, B., et al.: Co-teaching: robust training of deep neural networks with extremely noisy labels. Adv. Neural Inf. Process. Syst. 31 (2018)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778 (2016)
Javadi, G., et al.: Training deep networks for prostate cancer diagnosis using coarse histopathological labels. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12908, pp. 680–689. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87237-3_65
Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. Adv. Neural Inf. Process. Syst. 30 (2017)
Rai, B.P., Mayerhofer, C., Somani, B.K., Kallidonis, P., Nagele, U., Tokas, T.: Magnetic resonance imaging/ultrasound fusion-guided transperineal versus magnetic resonance imaging/ultrasound fusion-guided transrectal prostate biopsy-a systematic review. Eur. Urol. Oncol. 4(6), 904–913 (2021)
Rohrbach, D., Wodlinger, B., Wen, J., Mamou, J., Feleppa, E.: High-frequency quantitative ultrasound for imaging prostate cancer using a novel micro-ultrasound scanner. Ultrasound in Med. Biol. 44(7), 1341–1354 (2018)
Sensoy, M., Kaplan, L., Kandemir, M.: Evidential deep learning to quantify classification uncertainty. Adv. Neural Inf. Process. Syst. 31 (2018)
Shao, Y., Wang, J., Wodlinger, B., Salcudean, S.E.: Improving prostate cancer (PCA) classification performance by using three-player minimax game to reduce data source heterogeneity. IEEE Trans. Med. Imaging 39(10), 3148–3158 (2020)
Siddiqui, M.M., et al.: Magnetic resonance imaging/ultrasound-fusion biopsy significantly upgrades prostate cancer versus systematic 12-core transrectal ultrasound biopsy. Eur. Urol. 64(5), 713–719 (2013)
Smith, L., Bryan, S., De, P., et al.: Canadian cancer statistics advisory committee. Can. Can. Stat. 2018 (2018)
Sountoulides, P.: Micro-ultrasound-guided vs multiparametric magnetic resonance imaging-targeted biopsy in the detection of prostate cancer: a systematic review and meta-analysis. J. Urol. 205(5), 1254–1262 (2021)
To, M.N.N., et al.: Increasing diagnostic yield of prostate cancer during ultrasound guided biopsy in the presence of label noise (2022)
To, M.N.N., et al.: Coarse label refinement for improving prostate cancer detection in ultrasound imaging. Int. J. Comput. Assis. Radiol. Surg. 17(5), 841–847 (2022)
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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).
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Gilany, M. et al. (2022). Towards Confident Detection of Prostate Cancer Using High Resolution Micro-ultrasound. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13434. Springer, Cham. https://doi.org/10.1007/978-3-031-16440-8_40
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