Focusing on Clinically Interpretable Features: Selective Attention Regularization for Liver Biopsy Image Classification

Chong Yin, Siqi Liu, Rui Shao, Pong C. Yuen

Abstract: Liver biopsy image analysis is the gold standard for early diagnosis of non-alcoholic fatty liver disease (NAFLD) worldwide. Deep neural networks offer an effective tool for image analysis. However, when applying deep learning methods to smaller histological image datasets, the model may be distracted by dominant normal tissues and ignore critical tissue alterations that pathologists focus on. In this paper, we propose a selective attention regularization module (SAttenReg) to mimic the diagnosis process of pathologists. Specifically, to explicitly encourage the model to focus on clinically interpretable features (e.g., nuclei and fat droplets), SAttenReg learns the attention map with the regularization of clinically interpretable features. Furthermore, with the different contributions of histological features, the model can selectively focus on different histological features based on the distribution of nuclei in each instance. Experiments conducted on the in-house Liver-NAS and public Biopsy4Grading biopsy image datasets show that our method achieves superior classification performance with promising localization results.