Multi-scale Convolutional Neural Network for Melanoma Histopathology Image Classification

Abstract

Pathologists need to comprehensively observe histopathological images of different scales in clinical melanoma diagnosis because histopathological images have various distinctive features at different scales. In clinical diagnosis, the combination of 40X and 10X pathological images is the most commonly used by pathologists. ResNet50 is a popular convolutional neural network structure, which is often used in automatic diagnosis. However, the current research on deep learning-based histopathological image recognition mainly focuses on single-scale networks, and there are few studies on multi-scale neural networks. Therefore, we propose a novel multi-scale convolutional network based on ResNet50. It can merge features of different scales by simultaneously input images of 40x and 10x. Furthermore, we collected 2,241 Whole slide images and constructed a multi-center melanoma dataset for model training and testing. In the experiment, the accuracy of the multi-scale network is 96.4%, and the accuracy of single-scale networks is 90.3%∼95.4%. The multi-scale network has achieved significantly better performance than single-scale networks. Experimental results show that, compared with single-scale networks, multi-scale neural networks can learn more sufficient features and obtain higher diagnostic accuracy.