Adaptive Candidate Generation and Multilevel Domain Adversarial for Mitosis Detection in Breast Cancer

To address the domain shift problem caused by inconsistent staining protocols and scanner hardware between different laboratories in mitosis detection task, this study proposes an adaptive candidate generation and multi-level domain adversarial method. An adversarial learning strategy with image-level and center-aware instance-level domain adaptation is proposed to implement alignment of global features and foreground object features. For detecting small mitotic cells, a multi-scale coarse-fine regression scheme is developed based on the feature pyramid network, which adopts a dynamic anchor selection strategy to mine potential anchors across varying instance sizes, thereby achieving adaptive region proposal generation. Finally, joint optimization of classification confidence and regression precision is conducted to produce robust detection outputs. Experimental results demonstrate performance improvements of 0.029 on MIDOG 2021 compared with baseline methods, along with achieved scores of 0.675 and 0.833 on ICPR MITOS 2014 and ICPR MITOS 2012 datasets respectively, and this proposal are superior to comparative approaches.