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.
翻译:光学图象探测是数字病理学中一项具有挑战性的任务,直接影响到治疗决定。虽然自动化方法往往在实验室条件下取得可接受的结果,但在临床部署阶段却经常失败。这个问题主要可归因于所谓的域变现象。不同显微镜及其摄像系统引入了一个重要的域变源,它们明显改变了数字化图像的颜色表现。在这个方法说明中,我们介绍了我们提交的Mitsocid Domain通用挑战的算法。Mitosis Domain Generalization挑战使用了一个经过强有力数据增强训练的RetinaNet,在初步测试集中获得了0.7138的F1分。