Learning Sparse Interpretable Features For NAS Scoring From Liver Biopsy Images

Abstract

Liver biopsy images play a key role in the diagnosis of global non-alcoholic fatty liver disease (NAFLD). The NAFLD activity score (NAS) on liver biopsy images grades the amount of histological findings that reflect the progression of NAFLD. However, liver biopsy image analysis remains a challenging task due to its complex tissue structures and sparse distribution of histological findings. In this paper, we propose a sparse interpretable feature learning method (SparseX) to efficiently estimate NAS scores. First, we introduce an interpretable spatial sampling strategy based on histological features to effectively select informative tissue regions containing tissue alterations. Then, SparseX formulates the feature learning as a low-rank decomposition problem. Non-negative matrix factorization (NMF)-based attributes learning is embedded into a deep network to compress and select sparse features for a small portion of tissue alterations contributing to diagnosis. Experiments conducted on the internal Liver-NAS and public SteatosisRaw datasets show the effectiveness of the proposed method in terms of classification performance and interpretability. regions containing tissue alterations. Then, SparseX formulates the feature learning as a low-rank decomposition problem. Non-negative matrix factorization (NMF)-based attributes learning is embedded into a deep network to compress and select sparse features for a small portion of tissue alterations contributing to diagnosis. Experiments conducted on the internal Liver-NAS and public SteatosisRaw datasets show the effectiveness of the proposed method in terms of classification performance and interpretability.