Self-knowledge distillation for prediction of breast cancer molecular subtypes based on digital breast tomosynthesis.

This study aims to investigate the effectiveness of self-knowledge distillation (self-KD) with progressive refinement in the early prediction of molecular subtypes of breast cancer (BC) using digital breast tomosynthesis (DBT) images. This study conducted a retrospective analysis of 368 patients who underwent breast DBT and/or magnetic resonance imaging (MRI) scans at our hospital. Among these patients, 303 underwent DBT scans and 119 underwent MRI scans. Of the DBT patients, 137 had images with molecular subtypes labels, while the remaining 166 did not have molecular subtype annotations. None of the MRI patients had the corresponding molecular subtype labels. To address the issue of insufficient labeled DBT images, we proposed a self-knowledge distillation (self-KD) framework with progressive refinement to more effectively utilize the unlabeled MRI and DBT image. Initially, the teacher network was pre-trained using unlabeled MRI images to capture the essential characteristics of BC. Subsequently, the teacher network was progressively refined to generate more accurate soft labels for the unlabeled DBT images, which improved the performance of the student network through KD. Additionally, a noise-adaptive layer was integrated to adjust the soft labels for more accurate learning. The performance of our method was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE) values. The proposed self-KD method achieved an AUC of 0.834, ACC of 0.732, SEN of 0.930, and SPE of 0.734, which surpassed the existing methods for BC molecular subtype prediction. Specifically, compared to the baseline KD, our self-KD improved AUC by 9%, ACC by 6%, SEN by 26%, and SPE by 9%. The proposed self-KD framework effectively refines the network using both labeled and unlabeled images, which enables more accurate BC molecular subtype prediction.