Liver fibrosis automatic diagnosis utilizing dense-fusion attention contrastive learning network.

Abstract

Liver fibrosis poses a significant public health challenge given its elevated incidence and associated mortality rates. Diffusion-Weighted Imaging (DWI) serves as a non-invasive diagnostic tool for supporting the identification of liver fibrosis. Deep learning, as a computer-aided diagnostic technology, can assist in recognizing the stage of liver fibrosis by extracting abstract features from DWI images. However, gathering samples is often challenging, posing a common dilemma in previous research. Moreover, previous studies frequently overlooked the cross-comparison information and latent connections among different DWI parameters. Thus, it is becoming a challenge to identify effective DWI parameters and dig potential features from multiple categories in a dataset with limited samples. A self-defined Multi-view Contrastive Learning Network is developed to automatically classify multi-parameter DWI images and explore synergies between different DWI parameters. A Dense-fusion Attention Contrastive Learning Network (DACLN) is designed and used to recognize DWI images. Concretely, a multi-view contrastive learning framework is constructed to train and extract features from raw multi-parameter DWI. Besides, a Dense-fusion module is designed to integrate feature and output predicted labels. We evaluated the performance of the proposed model on a set of real clinical data and analyzed the interpretability by Grad-CAM and annotation analysis, achieving average scores of 0.8825, 0.8702, 0.8933, 0.8727, and 0.8779 for accuracy, precision, recall, specificity and F-1 score. Of note, the experimental results revealed that IVIM-f, CTRW-β, and MONO-ADC exhibited significant recognition ability and complementarity. Our method achieves competitive accuracy in liver fibrosis diagnosis using the limited multi-parameter DWI dataset and finds three types of DWI parameters with high sensitivity for diagnosing liver fibrosis, which suggests potential directions for future research.