Autonomous Extraction of Gleason Patterns for Grading Prostate Cancer using Multi-Gigapixel Whole Slide Images

Prostate cancer (PCa) is the second deadliest form of cancer in males. The severity of PCa can be clinically graded through the Gleason scores obtained by examining the structural representation of Gleason cellular patterns. This paper presents an asymmetric encoder-decoder model that integrates a novel hierarchical decomposition block to exploit the feature representations pooled across various scales and then fuses them together to generate the Gleason cellular patterns using the whole slide images. Furthermore, the proposed network is penalized through a novel three-tiered hybrid loss function which ensures that the proposed model accurately recognizes the cluttered regions of the cancerous tissues despite having similar contextual and textural characteristics. We have rigorously tested the proposed network on 10,516 whole slide scans (containing around 71.7M patches), where the proposed model achieved 3.59\% improvement over state-of-the-art scene parsing, encoder-decoder, and fully convolutional networks in terms of intersection-over-union.