Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation
- URL: http://arxiv.org/abs/2402.16280v2
- Date: Wed, 28 Feb 2024 01:49:16 GMT
- Title: Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation
- Authors: Yu Ming, Zihao Wu, Jie Yang, Danyi Li, Yuan Gao, Changxin Gao,
Gui-Song Xia, Yuanqing Li, Li Liang and Jin-Gang Yu
- Abstract summary: 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.
Extensive experiments on a couple of publicly accessible datasets demonstrate that SGFSIS can outperform other annotation-efficient learning baselines.
- Score: 50.407071700154674
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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