A hierarchical conditional random field-based attention mechanism approach for gastric histopathology image classification

• DOI: 10.1007/s10489-021-02886-2
• Publication Date: 2022-01-08T00:03:13Z
• Authors: Yixin Li; Xinran Wu; Chen Li; Xiaoyan Li; Haoyuan Chen; Changhao Sun; Md Mamunur Rahaman; Yudong Yao; Yong Zhang; Tao Jiang
• Keywords: FOS: Computer and information sciences; 03 medical and health sciences; 0302 clinical medicine; Computer Vision and Pattern Recognition (cs.CV); Computer Science - Computer Vision and Pattern Recognition; 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology

In the Gastric Histopathology Image Classification (GHIC) tasks, which are usually weakly supervised learning missions, there is inevitably redundant information in the images. Therefore, designing networks that can focus on effective distinguishing features has become a popular research topic. In this paper, to accomplish the tasks of GHIC superiorly and to assist pathologists in clinical diagnosis, an intelligent Hierarchical Conditional Random Field based Attention Mechanism (HCRF-AM) model is proposed. The HCRF-AM model consists of an Attention Mechanism (AM) module and an Image Classification (IC) module. In the AM module, an HCRF model is built to extract attention regions. In the IC module, a Convolutional Neural Network (CNN) model is trained with the attention regions selected and then an algorithm called Classification Probability-based Ensemble Learning is applied to obtain the image-level results from patch-level output of the CNN. In the experiment, a classification specificity of 96.67% is achieved on a gastric histopathology dataset with 700 images. Our HCRF-AM model demonstrates high classification performance and shows its effectiveness and future potential in the GHIC field.