An Explainable Graph Neural Network Approach for Patch Selection Using a New Patch Score Metric in Breast Cancer Detection

Abstract

This study aims to develop an algorithm for selecting the most informative and diverse patches from breast histopathology images while excluding irrelevant areas to enhance cancer detection. A key contribution of the method is the creation of a new metric called patch score that integrates SHAP values with Haralick features, improving both explainability and diagnostic accuracy. The algorithm begins by calculating Haralick features and measuring cosine similarity between patches to construct a graph, which is then used to train a graph neural network (GNN). To assess each patch's contribution to the analysis, we employ a SHAP explainer on the GNN model. The SHAP values and the features from each patch are then used to calculate a score called the patch score, which determines the importance of each patch. Additionally, to incorporate diversity in the selected patches, all patches are clustered based on local binary patterns, and the patch with the highest patch score from each cluster is selected to obtain the final patches for image classification. Features extracted from these patches using a ResNeXt 50 model, fused with 3-norm pooling, are used to classify the images as benign or malignant. The proposed framework was evaluated on the BreakHis dataset and demonstrated superior accuracy and precision compared to existing methods. By integrating both explainability and diversity into patch selection, the algorithm delivers a robust, interpretable model, offering dependable diagnostic support for pathologists.