Deep Learning Framework for Managing Inter-Reader Variability in Background Parenchymal Enhancement Classification for Contrast-Enhanced Mammography

Background parenchymal enhancement (BPE) classification for contrast-enhanced mammography (CEM) is highly affected by inter-reader variability. Traditional approaches aggregate expert annotations into a single consensus label to minimize individual subjectivity. By contrast, we propose a two-stage deep learning framework that explicitly models inter-reader variability through self-trained, reader-specific embeddings. In the first stage, the model learns discriminative image features while associating each reader with a dedicated embedding that captures their annotation signature, enabling personalized BPE classification. In the second stage, these embeddings can be calibrated using a small set of CEM cases selected through active learning and annotated by either a new reader or a consensus standard. This calibration process allows the model to adapt to new annotation styles with minimal supervision and without extensive retraining. This work leverages a multi-site CEM dataset of 7,734 images, non-exhaustively annotated by several readers. Calibrating reader-specific embeddings using a set of 40 cases offers an average accuracy of 73.5%, outperforming the proposed baseline method based on reader consensus. This approach enhances robustness and generalization in clinical environments characterized by heterogeneous labeling patterns.