Transfer Learning Strategy Based on Unsupervised Learning and Ensemble Learning for Breast Cancer Molecular Subtype Prediction Using Dynamic Contrast‐Enhanced MRI

Background Imaging‐driven deep learning strategies focus on training from scratch and transfer learning. However, the performance of training from scratch is often impeded by the lack of large‐scale labeled training data. Additionally, owing to the differences between source and target domains, analyzing medical image tasks satisfactorily via transfer learning based on ImageNet is difficult. Purpose To investigate two transfer learning algorithms for breast cancer molecular subtype prediction (luminal and non‐luminal) based on unsupervised pre‐training and ensemble learning: M_EL and B_EL, using malignant and benign datasets as the source domain, respectively. Study Type Retrospective. Population Eight hundred and thirty‐three female patients with histologically confirmed breast lesions (567 benign and 266 malignant cases) were selected. In the 5‐fold cross‐validation, the malignant cohort was randomly divided into 5 subsets to form a training set (80%) and a validation set (20%). Field Strength/Sequence 3.0 T, dynamic contrast‐enhanced magnetic resonance imaging ( DCE ‐ MRI) using T1 ‐weighted high‐resolution isotropic volume examination. Assessment First, three datasets acquired at different times post‐contrast were preprocessed as unlabeled source domains. Second, three baseline networks corresponding to the different MRI post‐contrast phases were built, optimized by a combination of mutual information maximization between high‐ and low‐level representations and prior distribution constraints. Next, the pre‐trained networks were fine‐tuned on the labeled target domain. Finally, prediction results were integrated using weighted voting‐based ensemble learning. Statistical Tests Mean accuracy, precision, specificity, and area under receiver operating characteristic curve (AUC) were obtained with 5‐fold cross‐validation. P < 0.05 was considered to be statistically significant. Results Compared with a convolutional long short‐term memory network, pre‐trained VGG‐16, VGG‐19, and DenseNet‐121 from ImageNet, M_EL and B_EL exhibited significantly more optimized prediction performance (specificity: 90.5% and 89.9%; accuracy: 82.6% and 81.1%; precision: 91.2% and 90.9%; AUC: 0.836 and 0.823, respectively). Data Conclusion Transfer learning based on unsupervised pre‐training may facilitate automatic prediction of breast cancer molecular subtypes. Level of Evidence 3 Technical Efficacy Stage 2