论文标题
多任务视网膜有丝分裂检测
Multi tasks RetinaNet for mitosis detection
论文作者
论文摘要
有丝分裂细胞的描述是肿瘤诊断的关键特征。但是，由于有丝分裂细胞形态的差异，检测肿瘤组织中有丝分裂细胞是一项高度挑战的任务。同时，尽管先进的深度学习方法在细胞检测方面取得了巨大的成功，但从另一个域（即不同的肿瘤类型和不同的扫描仪）测试数据时，性能通常是不令人满意的。因此，有必要开发用于检测域中具有稳健性的有丝分裂细胞的算法。我们的工作进一步提出了基于基线（视网膜）的前景检测和肿瘤分类任务，并利用数据扩展来改善模型的域泛化性能。我们在具有挑战性的前测试数据集上实现了最先进的性能（F1分数：0.5809）。
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.
