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

Show abstract - Show schedule - PDF - Reviews

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.
Hide abstract

Wednesday 7th July
D4-12 (short): Detection and Diagnosis 1 - 16:45 - 17:30 (UTC+2)
Hide schedule

Can't display slides, your browser doesn't support embedding PDFs.

Download slides