多任务学习提升病理图像中异常有丝分裂识别的跨域稳定性
A multi-task neural network for atypical mitosis recognition under domain shift
- 通过辅助任务引导模型关注目标区域，忽略背景差异
- 在三个数据集上均表现稳定，跨域性能显著优于基线
- 适合需要跨医院/设备部署的病理辅助诊断场景
在组织病理图像中识别异常有丝分裂有助于医生准确评估肿瘤侵袭性。尽管机器学习模型可自动完成此任务，但在域偏移下其性能会显著下降。本文提出一种基于多任务学习的方法，利用与主分类任务相关的辅助任务，帮助模型聚焦于待分类对象，忽略图像中的域相关背景变化。该方法参与了MItosis DOmain Generalization（MIDOG）挑战赛2025年第二赛道，在三个不同数据集——MIDOG 2025异常训练集、Ami-Br数据集以及MIDOG25初步测试集上的初步评估中展现出良好性能。
原文摘要 · Abstract (English)
Recognizing atypical mitotic figures in histopathology images allows physicians to correctly assess tumor aggressiveness. Although machine learning models could be exploited for automatically performing such a task, under domain shift these models suffer from significative performance drops. In this work, an approach based on multi-task learning is proposed for addressing this problem. By exploiting auxiliary tasks, correlated to the main classification task, the proposed approach, submitted to the track 2 of the MItosis DOmain Generalization (MIDOG) challenge, aims to aid the model to focus only on the object to classify, ignoring the domain varying background of the image. The proposed approach shows promising performance in a preliminary evaluation conducted on three distinct datasets, i.e., the MIDOG 2025 Atypical Training Set, the Ami-Br dataset, as well as the preliminary test set of the MIDOG25 challenge.
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