AI-assisted nuclei segmentation in histopathological images is a crucial task in the diagnosis and treatment of cancer diseases. It decreases the time required to manually screen microscopic tissue images and can resolve the conflict between pathologists during diagnosis. Deep Learning has proven useful in such a task. However, lack of labeled data is a significant barrier for deep learning-based approaches. In this study, we propose a novel approach to nuclei segmentation that leverages the available labelled and unlabelled data. The proposed method combines the strengths of both transductive and inductive learning, which have been previously attempted separately, into a single framework. Inductive learning aims at approximating the general function and generalizing to unseen test data, while transductive learning has the potential of leveraging the unlabelled test data to improve the classification. To the best of our knowledge, this is the first study to propose such a hybrid approach for medical image segmentation. Moreover, we propose a novel two-stage transductive inference scheme. We evaluate our approach on MoNuSeg benchmark to demonstrate the efficacy and potential of our method.

翻译：在癌症疾病的诊断与治疗中，基于人工智能的细胞核分割是组织病理学图像分析的关键任务。该方法能减少人工筛查显微组织图像所需时间，并可在病理学家诊断时化解意见分歧。深度学习已被证明在此类任务中具有显著效用，然而标注数据匮乏成为深度学习方法的重大障碍。本研究提出一种创新的细胞核分割方法，可同时利用已标注与未标注数据。该方案将此前分别探索的直推式学习与归纳式学习优势融合于统一框架：归纳式学习旨在逼近通用函数并泛化至未见测试数据，而直推式学习则可利用未标注测试数据提升分类性能。据我们所知，这是首个将此类混合方法应用于医学图像分割的研究。此外，我们提出了一种新颖的两阶段直推式推理方案。通过在MoNuSeg基准数据集上的评估，验证了该方法的有效性与潜力。