跨器官跨扫描仪癌细胞分割，用跨任务预训练提升泛化能力。
Cross-Task Pretraining for Cross-Organ Cross-Scanner Adenocarcinoma Segmentation
- 用跨任务预训练策略缓解不同显微镜和器官间的图像差异。
- 在未见器官和扫描仪数据上表现更优，优于单一训练或合并数据训练。
- 适合病理图像分析、医学影像模型泛化研究者参考。
本文针对COSAS 2024竞赛中跨器官、跨扫描仪腺癌分割任务提出解决方案。该任务的核心挑战在于更换显微镜设备或组织来源器官时出现显著的域偏移。竞赛包含两项任务：一是在三个器官的图像数据上训练，预测未见过的器官（数据集T1）的分割结果；二是在一个数据集上训练，对另一个未见扫描仪获取的图像进行分割。我们尝试了三种策略：对每个数据集单独训练、在数据集T1上预训练后微调至T2（反之亦然，称为交叉任务预训练）、以及联合训练两个数据集。实验表明，交叉任务预训练在域泛化方面更具优势，尤其在处理未知器官与未知扫描仪数据时表现更佳。
原文摘要 · Abstract (English)
This short abstract describes a solution to the COSAS 2024 competition on Cross-Organ and Cross-Scanner Adenocarcinoma Segmentation from histopathological image patches. The main challenge in the task of segmenting this type of cancer is a noticeable domain shift encountered when changing acquisition devices (microscopes) and also when tissue comes from different organs. The two tasks proposed in COSAS were to train on a dataset of images from three different organs, and then predict segmentations on data from unseen organs (dataset T1), and to train on a dataset of images acquired on three different scanners and then segment images acquired with another unseen microscope. We attempted to bridge the domain shift gap by experimenting with three different strategies: standard training for each dataset, pretraining on dataset T1 and then fine-tuning on dataset T2 (and vice-versa, a strategy we call \textit{Cross-Task Pretraining}), and training on the combination of dataset A and B. Our experiments showed that Cross-Task Pre-training is a more promising approach to domain generalization.
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