面向COSAS 2024挑战赛的跨器官与跨扫描仪腺癌分割的域分层训练
Domain-stratified Training for Cross-organ and Cross-scanner Adenocarcinoma Segmentation in the COSAS 2024 Challenge
AI总结：
本文提出一种器官与扫描仪分层的Upernet集成训练方法，用于COSAS 2024跨器官跨扫描仪腺癌分割，在任务1和任务2分别取得0.7643和0.8354的分数，展现了良好的泛化能力。
摘要：
本文介绍了一种为跨器官与跨扫描仪腺癌分割（COSAS 2024）挑战赛开发的图像分割算法。我们采用了器官分层和扫描仪分层的方法，训练了多个基于Upernet的分割模型，并对结果进行了集成。尽管不同器官的肿瘤特征各异以及不同扫描仪的成像条件不同带来了挑战，我们的方法在任务1中取得了0.7643的最终测试分数，在任务2中取得了0.8354的分数。这些结果证明了我们的方法在不同条件下的适应性和有效性。我们的模型在各种数据集上的泛化能力凸显了其在实际应用中的潜力。
英文摘要：
This manuscript presents an image segmentation algorithm developed for the Cross-Organ and Cross-Scanner Adenocarcinoma Segmentation (COSAS 2024) challenge. We adopted an organ-stratified and scanner-stratified approach to train multiple Upernet-based segmentation models and subsequently ensembled the results. Despite the challenges posed by the varying tumor characteristics across different organs and the differing imaging conditions of various scanners, our method achieved a final test score of 0.7643 for Task 1 and 0.8354 for Task 2. These results demonstrate the adaptability and efficacy of our approach across diverse conditions. Our model's ability to generalize across various datasets underscores its potential for real-world applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
