Two-stage Joint Transductive and Inductive learning for Nuclei Segmentation

• Main Authors: Ali, Hesham, Tondji, Idriss, Siam, Mennatullah
• Format: Journal Article
• Language: English
• Published: 15-11-2023
• Subjects: Computer Science - Computer Vision and Pattern Recognition; Computer Science - Learning
• DOI: 10.48550/arxiv.2311.08774

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.