Controlling False Positive/Negative Rates for Deep-Learning-Based Prostate Cancer Detection on Multiparametric MR Images

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12722)


Prostate cancer (PCa) is one of the leading causes of death for men worldwide. Multi-parametric magnetic resonance (mpMR) imaging has emerged as a non-invasive diagnostic tool for detecting and localising prostate tumours by specialised radiologists. These radiological examinations, for example, for differentiating malignant lesions from benign prostatic hyperplasia in transition zones and for defining the boundaries of clinically significant cancer, remain challenging and highly skill-and-experience-dependent. We first investigate experimental results in developing object detection neural networks that are trained to predict the radiological assessment, using these high-variance labels. We further argue that such a computer-assisted diagnosis (CAD) system needs to have the ability to control the false-positive rate (FPR) or false-negative rate (FNR), in order to be usefully deployed in a clinical workflow, informing clinical decisions without further human intervention. However, training detection networks typically requires a multi-tasking loss, which is not trivial to be adapted for a direct control of FPR/FNR. This work in turn proposes a novel PCa detection network that incorporates a lesion-level cost-sensitive loss and an additional slice-level loss based on a lesion-to-slice mapping function, to manage the lesion- and slice-level costs, respectively. Our experiments based on 290 clinical patients concludes that 1) The lesion-level FNR was effectively reduced from 0.19 to 0.10 and the lesion-level FPR was reduced from 1.03 to 0.66 by changing the lesion-level cost; 2) The slice-level FNR was reduced from 0.19 to 0.00 by taking into account the slice-level cost; (3) Both lesion-level and slice-level FNRs were reduced with lower FP/FPR by changing the lesion-level or slice-level costs, compared with post-training threshold adjustment using networks without the proposed cost-aware training. For the PCa application of interest, the proposed CAD system is capable of substantially reducing FNR with a relatively preserved FPR, therefore is considered suitable for PCa screening applications.


Prostate cancer Multi-parametric resonance images Object detection False negative reduction 



This work is supported by the Wellcome/EPSRC Centre for Interventional and Surgical Sciences (203145Z/16/Z). This work was supported by the International Alliance for Cancer Early Detection, a partnership between Cancer Research UK [C28070/A30912; C73666/A31378], Canary Center at Stanford University, the University of Cambridge, OHSU Knight Cancer Institute, University College London and the University of Manchester.


  1. 1.
    Cao, R., et al.: Joint prostate cancer detection and Gleason score prediction in mp-MRI via FocalNet. IEEE Trans. Med. Imaging 38(11), 2496–2506 (2019)CrossRefGoogle Scholar
  2. 2.
    Cao, R., et al.: Prostate cancer detection and segmentation in multi-parametric MRI via CNN and conditional random field. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 1900–1904. IEEE (2019)Google Scholar
  3. 3.
    Dai, Z., et al.: Segmentation of the prostatic gland and the intraprostatic lesions on multiparametic magnetic resonance imaging using mask region-based convolutional neural networks. Adv. Radiat. Oncol. 5(3), 473–481 (2020)CrossRefGoogle Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    Li, W., et al.: Path R-CNN for prostate cancer diagnosis and Gleason grading of histological images. IEEE Trans. Med. Imaging 38(4), 945–954 (2018)CrossRefGoogle Scholar
  6. 6.
    Litjens, G., Debats, O., Barentsz, J., Karssemeijer, N., Huisman, H.: Computer-aided detection of prostate cancer in MRI. IEEE Trans. Med. Imaging 33(5), 1083–1092 (2014)CrossRefGoogle Scholar
  7. 7.
    Liu, L., et al.: Deep learning for generic object detection: a survey. Int. J. Comput. Vis. 128(2), 261–318 (2020)CrossRefGoogle Scholar
  8. 8.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. arXiv preprint arXiv:1506.01497 (2015)
  9. 9.
    Saha, A., Hosseinzadeh, M., Huisman, H.: End-to-end prostate cancer detection in bpMRI via 3D CNNs: effect of attention mechanisms, clinical priori and decoupled false positive reduction. arXiv preprint arXiv:2101.03244 (2021)
  10. 10.
    Sanford, T., et al.: Deep-learning-based artificial intelligence for PI-RADS classification to assist multiparametric prostate MRI interpretation: a development study. J. Magn. Reson. Imaging 52(5), 1499–1507 (2020)CrossRefGoogle Scholar
  11. 11.
    Schelb, P., et al.: Classification of cancer at prostate MRI: deep learning versus clinical PI-RADS assessment. Radiology 293(3), 607–617 (2019)CrossRefGoogle Scholar
  12. 12.
    Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2020. CA Cancer J. Clin. 70(1), 7–30 (2020)CrossRefGoogle Scholar
  13. 13.
    Turkbey, B., Choyke, P.L.: PIRADS 2.0: what is new? Diagn. Interven. Radiol. 21(5), 382 (2015)CrossRefGoogle Scholar
  14. 14.
    Wildeboer, R.R., van Sloun, R.J., Wijkstra, H., Mischi, M.: Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods. Comput. Meth. Prog. Biomed. 189, 105316 (2020)CrossRefGoogle Scholar
  15. 15.
    Yu, X., et al.: False positive reduction using multiscale contextual features for prostate cancer detection in multi-parametric MRI scans. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 1355–1359. IEEE (2020)Google Scholar
  16. 16.
    Yu, X., et al.: Deep attentive panoptic model for prostate cancer detection using biparametric MRI scans. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12264, pp. 594–604. Springer, Cham (2020). Scholar

Copyright information

© Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.Centre for Medical Image Computing and Wellcome/EPSRC Centre for Interventional and Surgical SciencesUniversity College LondonLondonUK
  2. 2.Urological Research NetworkMiami LakesUSA
  3. 3.Focalyx TechnologiesMiamiUSA
  4. 4.City University of Hong KongHong KongChina

Personalised recommendations