A multi-sequence MRI-based hierarchical expert diagnostic method for the molecular subtype of breast cancer

Breast cancer is one of the cancers of deep concern worldwide, and the molecular subtype of breast cancer is significant for patients' treatment selection, and prognosis judgment. The application of multi-sequence MRI technology provides a new non-invasive companion diagnostic method for molecular subtypes of breast cancer, 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) methods and fine-grained classification methods based on MRI inputs have been proposed. However, most of these methods operate in a 'black-box' without a detailed explanation of the intermediate processes, resulting in a lack of interpretability of the breast cancer classification process. To address this problem, our study 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 codiagnosis 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.