Liver fibrosis automatic diagnosis utilizing dense‐fusion attention contrastive learning network

Abstract

AbstractBackgroundLiver 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.PurposeA self‐defined Multi‐view Contrastive Learning Network is developed to automatically classify multi‐parameter DWI images and explore synergies between different DWI parameters.MethodsA 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.ResultsWe 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.ConclusionOur 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.