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.

在诊断和治疗癌症疾病中，基于人工智能的组织病理学图像中的细胞核分割是一项至关重要的任务，它减少了手动筛查显微组织图像所需的时间，并且能够解决病理学家在诊断过程中的冲突。本研究提出了一种新颖的细胞核分割方法，利用了可用的带标签和未标签数据。该方法将之前分别尝试过的归纳学习和传导学习的优势结合到一个框架中。归纳学习旨在逼近泛函并推广到未见的测试数据，而传导学习则有利于利用未标签的测试数据来改善分类。据我们所知，这是第一项提出在医学图像分割中采用这种混合方法的研究。此外，我们提出了一种新颖的两阶段传导推理方案。我们在 MoNuSeg 基准上评估了我们的方法，以展示其效果和潜力。