标题： 用于领域转移下非典型分裂识别的多任务神经网络 显示英文标题

标题： A multi-task neural network for atypical mitosis recognition under domain shift

摘要： 在组织病理学图像中识别非典型的有丝分裂图像有助于医生正确评估肿瘤的侵袭性。 尽管可以利用机器学习模型自动执行此类任务，但在领域转移的情况下，这些模型会遭受显著的性能下降。 在本工作中，提出了一种基于多任务学习的方法来解决这个问题。 通过利用与主要分类任务相关的辅助任务，所提出的方法提交到MItosis DOmain通用性（MIDOG）挑战赛的赛道2，旨在帮助模型仅关注要分类的对象，忽略图像中随领域变化的背景。 所提出的方法在三个不同数据集上的初步评估中表现出良好的性能，即MIDOG 2025异常训练集、Ami-Br数据集以及MIDOG25挑战赛的初步测试集。 显示英文摘要

摘要： 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.