IL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach

by   Haoyuan Chen, et al.

In recent years, colorectal cancer has become one of the most significant diseases that endanger human health. Deep learning methods are increasingly important for the classification of colorectal histopathology images. However, existing approaches focus more on end-to-end automatic classification using computers rather than human-computer interaction. In this paper, we propose an IL-MCAM framework. It is based on attention mechanisms and interactive learning. The proposed IL-MCAM framework includes two stages: automatic learning (AL) and interactivity learning (IL). In the AL stage, a multi-channel attention mechanism model containing three different attention mechanism channels and convolutional neural networks is used to extract multi-channel features for classification. In the IL stage, the proposed IL-MCAM framework continuously adds misclassified images to the training set in an interactive approach, which improves the classification ability of the MCAM model. We carried out a comparison experiment on our dataset and an extended experiment on the HE-NCT-CRC-100K dataset to verify the performance of the proposed IL-MCAM framework, achieving classification accuracies of 98.98 respectively. In addition, we conducted an ablation experiment and an interchangeability experiment to verify the ability and interchangeability of the three channels. The experimental results show that the proposed IL-MCAM framework has excellent performance in the colorectal histopathological image classification tasks.


page 5

page 9

page 10

page 12

page 17

page 18

page 19


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

In the Gastric Histopathology Image Classification (GHIC) tasks, which i...

Global Attention Mechanism: Retain Information to Enhance Channel-Spatial Interactions

A variety of attention mechanisms have been studied to improve the perfo...

Enhancing Breast Cancer Classification Using Transfer ResNet with Lightweight Attention Mechanism

Deep learning models have revolutionized image classification by learnin...

Cost-effective Interactive Attention Learning with Neural Attention Processes

We propose a novel interactive learning framework which we refer to as I...

Weakly-supervised Generative Adversarial Networks for medical image classification

Weakly-supervised learning has become a popular technology in recent yea...

Learning of Frequency-Time Attention Mechanism for Automatic Modulation Recognition

Recent learning-based image classification and speech recognition approa...

Metastatic Cancer Image Classification Based On Deep Learning Method

Using histopathological images to automatically classify cancer is a dif...

Please sign up or login with your details

Forgot password? Click here to reset