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

Abstract

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 distinguishing features has become a popular research topic. In this paper, to accomplish the tasks of GHIC superiorly and 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 the 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. In addition, the AM module and transfer learning technique allow the network to generalize well to other types of image data except histopathology images, and we obtain 95.5% and 95.8% accuracies on IG02 and Oxford-IIIT Pet Datasets.