分器官分扫描仪训练模型，提升肺癌分割泛化能力
Domain-stratified Training for Cross-organ and Cross-scanner Adenocarcinoma Segmentation in the COSAS 2024 Challenge
- 按器官和扫描仪分组训练多个UperNet模型
- 任务1得分0.7643，任务2得分0.8354
- 适合跨设备、跨部位医学图像分割场景
本文针对跨器官、跨扫描仪肺腺癌分割挑战（COSAS 2024）提出一种图像分割算法。采用器官分层与扫描仪分层策略，训练多个基于UperNet的分割模型，并对结果进行集成。尽管不同器官间肿瘤特征差异大，且各扫描仪成像条件各异，该方法在任务1上取得0.7643的测试分数，在任务2上取得0.8354的测试分数。结果表明该方法在多样化条件下具备良好适应性与有效性，其跨数据集泛化能力为实际应用提供了潜力。
原文摘要 · Abstract (English)
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.
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