D4 - Predicting molecular subtypes of breast cancer using multimodal deep learning and incorporation of the attention mechanism
Tianyu Zhang, Luyi Han, Yuan Gao, Xin Wang, Regina Beets-Tan, Ritse Mann
Accurately determining the molecular subtype of breast cancer is an important factor for the prognosis of breast cancer patients, and can guide treatment selection. In this study, we report a multimodal deep learning with attention mechanism (MDLA) for predicting the molecular subtypes of breast cancer from mammography and ultrasound images. Incorporation of the attention mechanism improved diagnostic performance for predicting 4-class molecular subtypes with Matthews correlation coefficient (MCC) of 0.794. The MDLA can also discriminate between Luminal disease and non-luminal disease with areas under the receiver operating characteristic curve (AUC) of 0.855. This work thus provides a noninvasive imaging biomarker to predict the molecular subtypes of breast cancer.
Wednesday 7th July
D4-12 (short): Detection and Diagnosis 1 - 16:45 - 17:30 (UTC+2)