AI Performance in Screening Mammograms May Improve Through Multi-Resolution Data Augmentation

This paper integrated a multi-resolution strategy into two state-of-the-art AI models for cancer detection within a double reader breast screening program and determined whether tumour sizes affected the performance of the better AI model. Transfer learning and a multi-resolution strategy were conducted on the Globally-aware Multiple Instance Classifier (GMIC) and Global-Local Activation Maps (GLAM) models using two Australian mammographic databases. The specificity and sensitivity of these AI models, both with and without transfer learning and multi-resolution strategies, were evaluated on our database of 450 normal cases and 450 cancer cases. When transfer learning and multi-resolution strategy were incorporated, the GMIC model outperformed the GLAM model in terms of specificity and sensitivity. The performance of the GMIC and GLAM with transfer learning and multi-resolution strategy was best with 91.6% and 86.9% of sensitivity, outperforming its transfer learning only and pre-trained mode. The sensitivity of the two transfer learning AI models was significantly improved using the multi-resolution strategies. The GMIC with transfer learning and the multi-resolution strategy demonstrated similar performance on screening mammograms with smaller tumour sizes, compared with larger tumour sizes. The study also supports the potential of the AI models to assist radiologists interpreting mammograms within a double reader breast screening program.