Exploring Classification of Histological Disease Biomarkers From Renal Biopsy Images

Abstract

Identification of diseased kidney glomeruli and fibrotic regions remains subjective and time-consuming due to complete dependence on an expert kidney pathologist. In an attempt to automate the classification of glomeruli into normal and abnormal morphology and classification of fibrosis patches into mild, moderate and severe categories, we investigate three deep learning techniques: traditional transfer learning, pre-trained deep neural networks for feature extraction followed by supervised classification, and a novel Multi-Gaze Attention Network (MGANet) that uses multi-headed self-attention through parallel residual skip connections in a CNN architecture. Emperically, while the transfer learning models such as ResNet50, InceptionResNetV2, VGG19 and InceptionV3 acutely under-perform in the classification tasks, the Logistic Regression model augmented with features extracted from the InceptionResNetV2 shows promising results. Additionally, the experiments effectively ascertain that the proposed MGANet architecture outperforms both the former baseline techniques to establish the state of the art accuracy of 87.25% and 81.47% for glomerluli and fibrosis classification, respectively on the Renal Glomeruli Fibrosis Histopathological (RGFH) database.