Multi tasks RetinaNet for mitosis detection
This work addresses the need for robust mitosis detection algorithms in medical imaging to improve tumor diagnosis, though it appears incremental as it builds on existing methods like RetinaNet.
The paper tackled the problem of detecting mitotic cells in tumor tissues, which is challenging due to morphological variability and domain shifts, by proposing a multi-task RetinaNet with foreground detection and tumor classification tasks, achieving a state-of-the-art F1 score of 0.5809 on a preliminary test dataset.
The account of mitotic cells is a key feature in tumor diagnosis. However, due to the variability of mitotic cell morphology, it is a highly challenging task to detect mitotic cells in tumor tissues. At the same time, although advanced deep learning method have achieved great success in cell detection, the performance is often unsatisfactory when tested data from another domain (i.e. the different tumor types and different scanners). Therefore, it is necessary to develop algorithms for detecting mitotic cells with robustness in domain shifts scenarios. Our work further proposes a foreground detection and tumor classification task based on the baseline(Retinanet), and utilizes data augmentation to improve the domain generalization performance of our model. We achieve the state-of-the-art performance (F1 score: 0.5809) on the challenging premilary test dataset.