Cross-view Relation Networks for Mammogram Mass Detection
This addresses a critical need for more accurate early breast cancer detection in medical imaging, though it is incremental as it builds on existing region-based CNN methods.
The paper tackles the problem of detecting mass lesions in mammograms by jointly learning features from two paired views, proposing CVR-RCNN to capture relational information between them. Results show it outperforms state-of-the-art methods on both a private and public dataset.
Mammogram is the most effective imaging modality for the mass lesion detection of breast cancer at the early stage. The information from the two paired views (i.e., medio-lateral oblique and cranio-caudal) are highly relational and complementary, and this is crucial for doctors' decisions in clinical practice. However, existing mass detection methods do not consider jointly learning effective features from the two relational views. To address this issue, this paper proposes a novel mammogram mass detection framework, termed Cross-View Relation Region-based Convolutional Neural Networks (CVR-RCNN). The proposed CVR-RCNN is expected to capture the latent relation information between the corresponding mass region of interests (ROIs) from the two paired views. Evaluations on a new large-scale private dataset and a public mammogram dataset show that the proposed CVR-RCNN outperforms existing state-of-the-art mass detection methods. Meanwhile, our experimental results suggest that incorporating the relation information across two views helps to train a superior detection model, which is a promising avenue for mammogram mass detection.