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.

翻译：肿瘤诊断的一个关键特征是线性细胞的描述。然而,由于线性细胞形态的变异性,检测肿瘤组织中的线性细胞是一项极具挑战性的任务。与此同时,尽管先进的深层学习方法在细胞检测方面取得了巨大成功,但当从另一个领域(即不同的肿瘤类型和不同的扫描仪)测试数据时,性能往往不尽人意。因此,有必要制定算法,在域变换情景中以稳健的方式探测线性细胞。我们的工作进一步提议根据基线(Retinannet)进行地面探测和肿瘤分类,并利用数据增强来改进我们模型的广域性性能。我们实现了最先进的性能(F1分:0.5809),即具有挑战性的预科性测试数据集。