  Multiparametric magnetic resonance imaging (mp-MRI) has shown excellent
results in the detection of prostate cancer (PCa). However, characterizing
prostate lesions aggressiveness in mp-MRI sequences is impossible in clinical
practice, and biopsy remains the reference to determine the Gleason score (GS).
In this work, we propose a novel end-to-end multi-class network that jointly
segments the prostate gland and cancer lesions with GS group grading. After
encoding the information on a latent space, the network is separated in two
branches: 1) the first branch performs prostate segmentation 2) the second
branch uses this zonal prior as an attention gate for the detection and grading
of prostate lesions. The model was trained and validated with a 5-fold
cross-validation on an heterogeneous series of 219 MRI exams acquired on three
different scanners prior prostatectomy. In the free-response receiver operating
characteristics (FROC) analysis for clinically significant lesions (defined as
GS > 6) detection, our model achieves 69.0% $\pm$14.5% sensitivity at 2.9 false
positive per patient on the whole prostate and 70.8% $\pm$14.4% sensitivity at
1.5 false positive when considering the peripheral zone (PZ) only. Regarding
the automatic GS group
