Deep-ReAP: Deep Representations And Partial label learning for Multi-pathology Classification

Automated detection of pathology in images with multiple pathologies is one of the most challenging problems in medical diagnostics. The primary hurdles for automated systems include data imbalance across pathology categories and structural variations in pathological manifestations across patients. In this work, we present a novel method to detect a minimal dataset to train deep learning models that classify and explain multiple pathologies through the deep representations. We implement partial label learning with 1% false labels to identify the under-fit pathological categories that need further training followed by fine-tuning the deep representations. The proposed method identifies 54% of available training images as optimal for explainable classification of upto 7 pathological categories that can co-exist in 36 various combinations in retinal images, with overall precision/recall/F_β scores of 57%/87%/80%. Thus, the proposed method can lead to explainable inferencing for multi-label medical image data sets.