arXiv 2402.162802024-02-28cs.CV

面向标注高效细胞核实例分割的少样本学习

Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation

Pazhou Laboratory（琶洲实验室）；Zhujiang Hospital, Southern Medical University（南方医科大学珠江医院）；Nanfang Hospital, Southern Medical University（南方医科大学南方医院）；School of Electronic Information, Wuhan University（武汉大学电子信息学院）；School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院）；School of Computer Science, Wuhan University（武汉大学计算机学院）

Yu Ming, Zihao Wu, Jie Yang, Danyi Li, Yuan Gao, Changxin Gao, Gui-Song Xia, Yuanqing Li, Li Liang, Jin-Gang Yu

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AI总结：

本文将少样本学习用于标注高效的细胞核实例分割，提出GFSIS定义并引入结构引导机制构建SGFSIS框架，以少于5%标注达到接近全监督性能。

摘要：

从组织病理学图像中进行细胞核实例分割，面临细胞核实例标注极其费力且依赖专家的问题。作为该任务的一种有前景的解决方案，标注高效的深度学习范式近来吸引了大量研究兴趣，例如弱监督/半监督学习、生成对抗学习等。本文提出从少样本学习（FSL）的角度构建标注高效的细胞核实例分割。本研究的动机在于，随着计算病理学的繁荣，越来越多的全标注数据集已可公开获取，我们希望利用这些外部数据集来辅助目标数据集上的细胞核实例分割，而目标数据集仅有非常有限的标注。为实现这一目标，我们采用基于元学习的FSL范式；然而，在适配本任务之前，必须在两个重要方面对其进行定制。首先，由于新类别可能与外部数据集的类别不一致，我们将少样本实例分割（FSIS）的基本定义扩展为广义少样本实例分割（GFSIS）。其次，为应对细胞核分割的内在挑战，包括相邻细胞接触、细胞异质性等，我们进一步在GFSIS网络中引入结构引导机制，最终形成统一的结构引导广义少样本实例分割（SGFSIS）框架。在多个公开可获取数据集上的大量实验表明，SGFSIS能够优于其他标注高效学习基线，包括半监督学习、简单迁移学习等；在使用少于5%标注的情况下，其性能可与全监督学习相媲美。

英文摘要：

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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