A multitask deep representation for Gleason score classification to support grade annotations

Abstract

The Gleason grade system is the main standard to quantify the aggressiveness and progression of prostate cancer. Currently, exists a high disagreement among experts in the diagnosis and stratification of this disease. Deep learning models have emerged as an alternative to classify and support experts automatically. However, these models are limited to learn a rigid stratification rule that can be biased during training to a specific observer. Therefore, this work introduces an embedding representation that integrates an auxiliary task learning to deal with the high inter and intra appearance of the Gleason system. The proposed strategy implements as a main task a triplet loss scheme that builds a feature embedding space with respect to batches of positive and negative histological training patches. As an auxiliary task is added a cross-entropy that helps with inter-class variability of samples while adding robust representations to the main task. The proposed approach shows promising results achieving an average accuracy of 66% and 64%, for two experts without statistical difference. Additionally, reach and average accuracy of 73% in patches where both pathologists are agree, showing the robustness patterns learning from the approach.