Multi-scale information with attention integration for classification of liver fibrosis in B-mode US image. (March 2022) - Feng, Xiangfei; Chen, Xin; Dong, Changfeng; Liu, Yingxia; Liu, Zhong; Ding, Ruixin; Huang, Qinghua
Highlights: A novel network for the classification of liver fibrosis into five levels is proposed which can effectively improve the classification performance. The equipped pyramid structure uses a large receptive field to capture the detailed information from the vague and low contrast and signal-to-noise ratio US images. The proposed local consistency attention module can suppress the distractors and enhance the representation of the feature. Experimental results on the liver fibrosis dataset demonstrate the effectiveness of the proposed network with a local consistency attention mechanism. Abstract: Background and objective: Chronic hepatitis B (CHB) is one of the most common liver diseases in the world, which threats a lot to people's usual life. The increased deposition of fibrotic tissues in livers for patients with CHB may lead to the development of liver cirrhosis, hepatocellular carcinoma, or even liver failure. Accurate fibrosis staging is very important for the targeted treatment of liver fibrosis and its recovery. Methods: In this paper, we propose a new deep convolutional neural network (DCNN) with functions of multi-scale information extraction and attention integration for more accurate liver fibrosis classification from ultrasound (US) images. The proposed network uses two pyramid-structured CNN elements to extract multi-scale features from US images. Such a design significantly enlarges the receptive field of the convolution layer, such that more useful information can be explored by the neural network to associate with the final classification. Based on this, a new feature distillation method is also proposed to enhance the ability of deep features derived from multi-scale information. The proposed distillation method employs attention maps to automatically extract class-related features from multi-scale information, which effectively suppress the influence of potential distractors. Results: Experimental results on the US liver fibrosis dataset collected from 286 participants show that the proposed deep framework achieves promising classification performance. The proposed method achieves a classification accuracy of 95.66% on the test dataset. Conclusion: Our proposed framework could stage liver fibrosis highly accurately. It might provide effective suggestions for the clinical treatment of liver fibrosis that can facilitate its recovery.