A Hierarchical Conditional Random Field-based Attention Mechanism Approach for Gastric Histopathology Image Classification

TL;DR

This paper introduces a hierarchical conditional random field-based attention mechanism to improve gastric histopathology image classification, achieving high accuracy and aiding clinical diagnosis.

Contribution

The paper proposes a novel HCRF-based attention mechanism integrated with CNNs for better focus on relevant features in weakly supervised gastric histopathology images.

Findings

Achieved 96.67% classification specificity on a gastric dataset.

Demonstrated high classification performance and effectiveness of the proposed model.

Showed potential for clinical application in gastric histopathology diagnosis.

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 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.