MitoDet: Simple and robust mitosis detection
This addresses the challenge of reliable mitosis detection for clinical decision-making, but it is incremental as it applies an existing method with enhancements to a known bottleneck.
The paper tackled the problem of domain shift in mitosis detection for digital pathology, which causes automated methods to fail in clinical deployment, and achieved an F1 score of 0.7138 on a test set using a RetinaNet with strong data augmentation.
Mitotic figure detection is a challenging task in digital pathology that has a direct impact on therapeutic decisions. While automated methods often achieve acceptable results under laboratory conditions, they frequently fail in the clinical deployment phase. This problem can be mainly attributed to a phenomenon called domain shift. An important source of a domain shift is introduced by different microscopes and their camera systems, which noticeably change the color representation of digitized images. In this method description we present our submitted algorithm for the Mitosis Domain Generalization Challenge, which employs a RetinaNet trained with strong data augmentation and achieves an F1 score of 0.7138 on the preliminary test set.