文献类型：期刊文献

英文题名：Context-Aware Cutmix is All You Need forUniversal Organ andCancer Segmentation

作者：Zhou, Qin[1,2]; Liu, Peng[1]; Zheng, Guoyan[1]

机构：[1] Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, No. 800, Dongchuan Road, Shanghai, 200240, China; [2] Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China

年份：2024

卷号：14544 LNCS

起止页码：28

外文期刊名：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

收录：EI(收录号：20243216808707)

语种：英文

外文关键词：Diseases

摘要：Due to its important potential for various clinical applications, universal organ and cancer segmentation has attracted increasing attention recently. However, its performance is largely hindered due to issues such as (1) partial and noisy labels from different sources and (2) tremendously heterogeneous tumor cases. In this paper, we propose a novel partially supervised segmentation framework by introducing the merge-max operation for hard mining among the unlabeled classes. Besides, to take full advantage of the expertly annotated tumor data, we design a novel context-aware CutMix scheme to dynamically perform tumor augmentation during training. We also introduce a useful data-cleaning strategy for self-training and adjust the nnU-Net framework for better efficiency. The average scores of organ DSC, organ NSD, tumor DSC and tumor NSD on the public validation set are 92.18%, 96.33%, 46.26% and 38.65%, respectively. And we achieve scores of 93.17% (organ DSC), 96.76% (organ NSD), 61.49% (tumor DSC) and 49.9% (tumor NSD) on the official test set. The average inference time is 13.95s, the average maximum GPU memory is 2823 MB, and the average area under the GPU memory-time curve is 14112. Collectively, we ranked second among all submitted teams. Our code is available at https://github.com/luckieucas/FLARE23. ? The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.