Nucleus instance segmentation from histopathology images suffers from the
extremely laborious and expert-dependent annotation of nucleus instances. As a
promising solution to this task, annotation-efficient deep learning paradigms
have recently attracted much research interest, such as weakly-/semi-supervised
learning, generative adversarial learning, etc. In this paper, we propose to
formulate annotation-efficient nucleus instance segmentation from the
perspective of few-shot learning (FSL). Our work was motivated by that, with
the prosperity of computational pathology, an increasing number of
fully-annotated datasets are publicly accessible, and we hope to leverage these
external datasets to assist nucleus instance segmentation on the target dataset
which only has very limited annotation. To achieve this goal, we adopt the
meta-learning based FSL paradigm, which however has to be tailored in two
substantial aspects before adapting to our task. First, since the novel classes
may be inconsistent with those of the external dataset, we extend the basic
definition of few-shot instance segmentation (FSIS) to generalized few-shot
instance segmentation (GFSIS). Second, to cope with the intrinsic challenges of
nucleus segmentation, including touching between adjacent cells, cellular
heterogeneity, etc., we further introduce a structural guidance mechanism into
the GFSIS network, finally leading to a unified Structurally-Guided Generalized
Few-Shot Instance Segmentation (SGFSIS) framework. Extensive experiments on a
couple of publicly accessible datasets demonstrate that, SGFSIS can outperform
other annotation-efficient learning baselines, including semi-supervised
learning, simple transfer learning, etc., with comparable performance to fully
supervised learning with less than 5% annotations.

在这篇论文中，我们提出了一种基于元学习的结构引导广义少样本实例分割（SGFSIS）框架，用于有效标注细胞核实例分割。实验证明，SGFSIS 在少于 5％的标注数据情况下，优于其他注释效率学习方法，包括半监督学习和简单迁移学习，并且与完全监督学习相当。