@inproceedings{d625e64e6fb34cbc99a49f50cb08917b,
title = "Multi-task RetinaNet for Mitosis Detection",
abstract = "The count of mitotic cells is a key feature in tumor diagnosis. However, due to the variability of mitotic cell morphology, detecting mitotic cells in tumor tissues is a highly challenging task. At the same time, the performance of the trained models often declines when there is a vast difference between the source domain and the target domain. (i.e., the different tumor types and scanners). Therefore, it is necessary to develop algorithms for detecting mitotic cells with robustness in domain shift scenarios. Our work proposes a foreground detection and tumor classification task based on the baseline (Retinanet) and utilizes data augmentation to improve our model{\textquoteright}s detection ability and domain generalization performance. We achieve excellent performance on the challenging preliminary test dataset (F1 score: 0.5809) and the Final test dataset (F1:0.6300).",
keywords = "Mitosis Detection, Multi tasks, Object Detection",
author = "Ziyue Wang and Yang Chen and Zijie Fang and Hao Bian and Yongbing Zhang",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 25th International Conference on Medical Image Computing and Computer-Assisted Intervention , MICCAI 2022 ; Conference date: 18-09-2022 Through 22-09-2022",
year = "2023",
doi = "10.1007/978-3-031-33658-4\_25",
language = "英语",
isbn = "9783031336577",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "234--240",
editor = "Bin Sheng and Marc Aubreville",
booktitle = "Mitosis Domain Generalization and Diabetic Retinopathy Analysis - MICCAI Challenges MIDOG 2022 and DRAC 2022, Held in Conjunction with MICCAI 2022, Proceedings",
address = "德国",
}