Artificial Intelligence can improve cancer detection in a double reading screening mammography scenario

This paper investigates whether two publicly available Artificial Intelligence (AI) models can detect retrospectively identified missed cancers within a double reader breast screening program and determine whether challenging mammographic cases are reflected in the performance of AI models. Transfer learning was conducted on the Globally-aware Multiple Instance Classifier (GMIC) and Global-Local Activation Maps (GLAM) models using an Australian mammographic dataset. Mammograms were enhanced to improve poor contrast using the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm. The sensitivity of the two AI models with pre-trained and transfer learning modes was evaluated on four mammographic case groups: 'missed' cancers, 'prior-visible' cancers, 'prior-invisible' cancers and 'current' cancers from the archives of a double reader breast screening program. The GMIC model outperformed the GLAM model with pre-trained and transfer learning modes in terms of sensitivity for all four cancer groups. The performance of the GMIC and GLAM models was best in 'prior-visible' cancers, followed by 'prior-invisible' cancers, 'current' cancers and 'missed' cancers. The performance of the GMIC and GLAM models on the 'missed' cancer cases was 84.2% and 81.5%, respectively while for the 'prior-visible' cancer cases, the performance was 92.7% and 89.2%, respectively. After transfer learning, both the GMIC and GLAM models demonstrated statistically significant improvement (>9.4%) in terms of sensitivity for all cancer groups. The AI models with transfer learning showed significant improvement in malignancy detection in challenging mammographic cases. The study also supports the potential of the AI models to identify missed cancers within a double reader breast screening program.