《3MT-Net: A Multi-modal Multi-task Model for Breast Cancer and Pathological Subtype Classification Based on a Multicenter Study》was accepted by JCR Q1 journal with an impact factor of 6.7

We are pleased to announce that the research paper "3MT-Net: A Multi-modal Multi-task Model for Breast Cancer and Pathological Subtype Classification Based on a Multicenter Study" has been accepted by the IEEE Journal of Biomedical and Health Informatics, a JCR Q1 journal with an impact factor of 6.7. This research introduces an innovative deep learning architecture named "Multi-modal Multi-task Network" (3MT-Net). Validation through multicenter datasets, 3MT-Net has significantly improved the accuracy of the diagnosis of benign and malignant breast tumors and the classification of pathological subtypes using clinical data as well as B-mode ultrasound and color Doppler ultrasound. The successful publication of this paper brings new insights and approaches to the field of AI-assisted diagnosis in breast cancer.