论文标题
MIDOG 2022挑战的径向预测域适应分类器
Radial Prediction Domain Adaption Classifier for the MIDOG 2022 Challenge
论文作者
论文摘要
本文描述了我们对MIDOG 2022挑战的贡献，以检测有丝分裂细胞。在MIDOG 2022挑战中要解决的主要问题之一是在组织病理学领域中现实生活中出现的自然方差下的鲁棒性。为了解决该问题，我们使用改编的Yolov5S模型与新的域自适应分类器（DAC）变体（radial-Prediction-dac）结合使用，以在域移动下实现稳健性。此外，我们使用HED色彩空间中的染色增强来增加可用训练数据的可变性。使用建议的方法，我们获得了0.6658的测试集F1得分。
This paper describes our contribution to the MIDOG 2022 challenge for detecting mitotic cells. One of the major problems to be addressed in the MIDOG 2022 challenge is the robustness under the natural variance that appears for real-life data in the histopathology field. To address the problem, we use an adapted YOLOv5s model for object detection in conjunction with a new Domain Adaption Classifier (DAC) variant, the Radial-Prediction-DAC, to achieve robustness under domain shifts. In addition, we increase the variability of the available training data using stain augmentation in HED color space. Using the suggested method, we obtain a test set F1-score of 0.6658.
