  Prostate cancer (PCa) is the second deadliest form of cancer in males, and it
can be clinically graded by examining the structural representations of Gleason
tissues. This paper proposes \RV{a new method} for segmenting the Gleason
tissues \RV{(patch-wise) in order to grade PCa from the whole slide images
(WSI).} Also, the proposed approach encompasses two main contributions: 1) A
synergy of hybrid dilation factors and hierarchical decomposition of latent
space representation for effective Gleason tissues extraction, and 2) A
three-tiered loss function which can penalize different semantic segmentation
models for accurately extracting the highly correlated patterns. In addition to
this, the proposed framework has been extensively evaluated on a large-scale
PCa dataset containing 10,516 whole slide scans (with around 71.7M patches),
where it outperforms state-of-the-art schemes by 3.22% (in terms of mean
intersection-over-union) for extracting the Gleason tissues and 6.91% (in terms
of F1 score) for grading the progression of PCa.
