MMDC-CLIP-F: Vision-language multi-view mammogram density classification with uncertainty assessment

Mammogram density classification is a critical component of breast cancer screening, as breast density is a well-established risk factor that also impacts the sensitivity of mammographic imaging. Traditional deep learning (DL) approaches, such as convolutional neural network (CNN) based models, have shown limitations in this domain, often struggling with poor inter-class differentiation and lacking the ability to leverage relational context between different mammographic views. To address these challenges, we propose a two-part framework for mammogram density assessment. The first component, Multi-View Mammogram Density Classification using Contrastive Language-Image Pretraining (MMDC-CLIP), combines the representational strength of vision–language models with multi-view fusion. Semantic prompts are used to inject domain-specific priors, enhancing feature discrimination, while randomized data augmentation mitigates the challenges of limited annotated datasets. The second component, a Multi-View Auxiliary Confidence Network (MV-ACN), processes the final hidden states from all views through a multi-head attention mechanism to generate calibrated confidence scores, enabling reliable identification of uncertain cases that may require secondary review. Together, MMDC-CLIP and MV-ACN form the proposed MMDC-CLIP-F framework. The MMDC-CLIP classifier using the CLIP ViT-L/14-336 backbone reaches 78.2% accuracy, 5.9 percentage points higher than MV-DEFEAT, and 91.5% multi-class AUC, an 8.9-percentage-point improvement, on the RSNA-SMBC dataset. MV-ACN further provides calibrated uncertainty estimates; when paired with MMDC-CLIP using the CLIP ViT-B/32 backbone, confidence stratification on RSNA-SMBC yields 93.9% accuracy in high-confidence samples compared with 52.4% in low-confidence samples. These calibrated confidence estimates enable downstream decision support, such as deferring low-reliability cases for radiologist review, thereby improving the safety and interpretability of automated mammogram density assessment.