MPSA: Multi-Position Supervised Soft Attention-based convolutional neural network for histopathological image classification

Abstract

In recent years, significant achievements have been made in the field of histopathological image analysis using convolutional neural networks (CNNs). However, existing CNNs fail to fully capture the important local structures and regional information in histopathological images due to the complex tissue structures and variable pathological features present in these images. They often treat all regions equally, which further exacerbates the challenge of accurately analyzing such images. Current network model can’t extract deep layer features efficiently without guiding. To alleviate this problem, we propose a novel network model called Multi-Position Supervised Soft Attention (MPSA). MPSA adds regions of interest (RoI) labels at multiple feature layers for deep supervision, and then uses the supervised layers as soft attention to guide the learning of the classification network, enabling the network to accurately extract features of the lesion target. Additionally, we design a Multi-level Attention Feature Enhancement Module (MAFEM), which combines multiple levels of attention mechanisms to enhance the performance of the convolutional neural network in histopathological image classification. MAFEM includes spatial attention, soft attention of the main branch, and our proposed soft attention for multi-branch feature fusion. The proposed soft attention for multi-branch feature fusion aims to enhance the predictive performance of the classification model by activating relevant neurons in the diagnostic area in a highly activated state, while effectively avoiding noise activation. This innovative approach ensures that the model can focus on the most pertinent information, leading to improved classification accuracy. We conducted classification experiments on the liver cancer histopathological images dataset and the results showed that our method achieved a classification accuracy of 95.79%, indicating that it is very effective in the analysis of liver histopathological images. Our proposed network architecture has also demonstrated good generalization ability in other medical datasets, achieving a classification accuracy of 84.41% on the ultrasound carotid plaque dataset.