Radial Prediction Domain Adaption Classifier for the MIDOG 2022 Challenge

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 us...

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Bibliographic Details
Published inarXiv.org
Main Authors Annuscheit, Jonas, Krumnow, Christian
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 12.09.2023
Subjects
Classifiers
Computer Science - Computer Vision and Pattern Recognition
Domains
Mitosis
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2208.13902