[1] Rawla, P. (2019). Epidemiology of prostate cancer. World journal of oncology, 10(2), 63.
[2] Epstein, J. I., Egevad, L., Amin, M. B., Delahunt, B., Srigley, J. R., & Humphrey, P. A. (2016). The 2014 International Society of Urological Pathology (ISUP) consensus conference on Gleason grading of prostatic carcinoma. The American journal of surgical pathology, 40(2), 244-252.
[3] Weinreb, J. C., Barentsz, J. O., Choyke, P. L., Cornud, F., Haider, M. A., Macura, K. J., ... & Verma, S. (2016). PI-RADS prostate imaging–reporting and data system: 2015, version 2. European urology, 69(1), 16-40.
[4] Hamoen, E. H., de Rooij, M., Witjes, J. A., Barentsz, J. O., & Rovers, M. M. (2015). Use of the prostate imaging reporting and data system (PI-RADS) for prostate cancer detection with multiparametric magnetic resonance imaging: a diagnostic meta-analysis. European urology, 67(6), 1112-1121.
[5] Armato, S. G., Huisman, H., Drukker, K., Hadjiiski, L., Kirby, J. S., Petrick, N., ... & Farahani, K. (2018). PROSTATEx Challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images. Journal of Medical Imaging, 5(4), 044501.
[6] Jaeger, P. F., Kohl, S. A., Bickelhaupt, S., Isensee, F., Kuder, T. A., Schlemmer, H. P., & Maier-Hein, K. H. (2020, April). Retina U-Net: Embarrassingly simple exploitation of segmentation supervision for medical object detection. In Machine Learning for Health Workshop (pp. 171-183). PMLR.
[7] Duran, A., Dussert, G., Rouviére, O., Jaouen, T., Jodoin, P. M., & Lartizien, C. (2022). ProstAttention-Net: a deep attention model for prostate cancer segmentation by aggressiveness in MRI scans. Medical Image Analysis, 102347.
[8] Cao, R., Bajgiran, A. M., Mirak, S. A., Shakeri, S., Zhong, X., Enzmann, D., ... & Sung, K. (2019). Joint prostate cancer detection and Gleason score prediction in mp-MRI via FocalNet. IEEE transactions on medical imaging, 38(11), 2496-2506.
[9] Saha, A., Hosseinzadeh, M., & Huisman, H. (2021). End-to-end prostate cancer detection in bpMRI via 3D CNNs: Effects of attention mechanisms, clinical priori and decoupled false positive reduction. Medical image analysis, 73, 102155.
[10] Bosma, J. S., Saha, A., Hosseinzadeh, M., Slootweg, I., de Rooij, M., & Huisman, H. (2021). Report-Guided Automatic Lesion Annotation for Deep Learning-Based Prostate Cancer Detection in bpMRI. arXiv preprint arXiv:2112.05151.
[11] Yu, X., Lou, B., Zhang, D., Winkel, D., Arrahmane, N., Diallo, M., ... & Kamen, A. (2020). Deep attentive panoptic model for prostate cancer detection using biparametric MRI scans. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 594-604). Springer, Cham.
[12] Greer, M. D., Shih, J. H., Lay, N., Barrett, T., Bittencourt, L., Borofsky, S., ... & Turkbey, B. (2019). Interreader variability of prostate imaging reporting and data system version 2 in detecting and assessing prostate cancer lesions at prostate MRI. AJR. American journal of roentgenology, 1.
[13] Smith, C. P., Harmon, S. A., Barrett, T., Bittencourt, L. K., Law, Y. M., Shebel, H., ... & Turkbey, B. (2019). Intra‐and interreader reproducibility of PI‐RADSv2: A multireader study. Journal of Magnetic Resonance Imaging, 49(6), 1694-1703.