The whole slide breast histopathology image detection based on a fused model and heatmaps

Abstract

The analysis of whole slide breast histopathology image is one of the most effective techniques in the current breast cancer diagnosis. However, the size of whole slide image(WSI) is too large to be processed directly by machine learning or deep learning based classifier. To solve the problem of WSI-based computer aided diagnosis, a novel classification framework with three stages is proposed in this paper to locate and classify tumor region in the whole slide images. This framework contains three parts: patch-based classification, tumor region segmentation and location, and WSI-based classification. In the period of data processing, the Cycle-GAN(Cycle Generative Adversarial Network) model is firstly adopted to normalize the colors of the images. Then an improved DPN68(Dual Path Networks) based classifier and a Swin-Transformer based classifier are combined to improve the accuracy of patch-based classification. With the malignant confidence probability output by the fused model, the whole slide heatmap is generated to segment, locate and visually display the tumor regions. Statistical features are computed and selected from the heatmap based on medical diagnosis indicators, and then an SVM(Support Vector Machines) based classifier is applied to implement WSI-based classification to further confirm the tumor. Experimental results on the Camelyon16 dataset show that our proposed framework can effectively classify the whole slide breast histopathology image with high-precision.