  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% and 99.77%,
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.

Computer Vision and Pattern Recognition