A Multi-Sequence MRI-Based Hierarchical Expert Diagnostic Method for the Molecular Subtype of Breast Cancer

Abstract

The molecular subtype of breast cancer is significant for patients' treatment and prognosis. The application of multi-sequence MRI technology provides a new non-invasive diagnostic method, which can more accurately assess the vascular status of tumors and reveal fine structures. However, providing interpretable classification results remains a challenge. Recently, although many convolutional neural network (CNN) and fine-grained classification methods based on MRI inputs have been proposed. However, most of these methods operate in a âblack-boxâ without a detailed explanation of the intermediate processes, resulting in a lack of interpretability of the breast cancer classification process. To address this problem, we proposes a multi-sequence MRI-based hierarchical expert diagnostic method for the molecular subtype of breast cancer. With the strong differentiation module, this method first identifies enhanced features in breast tumors, ensuring that the subsequent classification process is precisely focused on the lesion features. In addition, inspired by the co-diagnosis of multiple experts in clinical diagnosis, we set up a mechanism of collaborative diagnostic corrective learning by hierarchical experts to provide an interpretable classification process. Compared with previous studies, the framework learns features with a strong distinguishing ability for breast tumor classification. Specifically, multiple experts corrected each other's learning to give more accurate and interpretable classification results, significantly improving clinical diagnosis's practical value. We conducted extensive experiments on a breast dataset and compared it quantitatively with other methods, and we achieved the best performance in terms of accuracy (0.889) and F1 Score (0.893). We make the code public on GitHub: https://github.com/yanfangHao/HED.