用于语义分割的自监督少样本学习：一种免标注的方法
Self-supervised Few-shot Learning for Semantic Segmentation: An Annotation-free Approach
AI总结：
针对医学图像标注稀缺问题，提出免标注的自监督少样本语义分割框架，通过图划分任务分析特征向量估计掩码，并引入多尺度大核注意力模块提升分割效果。
摘要：
少样本语义分割（FSS）在医学图像分析领域具有巨大潜力，能够在有限训练数据下实现准确的物体分割。然而，现有的FSS技术严重依赖带标注的语义类别，由于标注稀缺，这使得它们不适用于医学图像。为应对这一挑战，本文提出了多项贡献：首先，受谱分解方法启发，将图像分解问题重新定义为图划分任务。通过分析从自监督网络的特征亲和矩阵导出的Laplacian矩阵的特征向量，来估计支持图像中感兴趣目标的分布。其次，我们提出了一种不依赖任何标注的新型自监督FSS框架。相反，它通过利用从支持图像获得的特征向量来自适应地估计查询掩码。这种方法消除了手动标注的需求，使其特别适用于标注数据有限的医学图像。第三，为了基于支持图像提供的信息进一步增强查询图像的解码，我们引入了多尺度大核注意力模块。通过选择性地强调相关特征和细节，该模块改善了分割过程，并有助于更好地描绘目标。在自然图像和医学图像数据集上的评估均证明了我们方法的高效性和有效性。此外，所提出的方法具有通用性和模型无关性，可与各种深度架构无缝集成。代码已在GitHub公开发布。
英文摘要：
Few-shot semantic segmentation (FSS) offers immense potential in the field of medical image analysis, enabling accurate object segmentation with limited training data. However, existing FSS techniques heavily rely on annotated semantic classes, rendering them unsuitable for medical images due to the scarcity of annotations. To address this challenge, multiple contributions are proposed: First, inspired by spectral decomposition methods, the problem of image decomposition is reframed as a graph partitioning task. The eigenvectors of the Laplacian matrix, derived from the feature affinity matrix of self-supervised networks, are analyzed to estimate the distribution of the objects of interest from the support images. Secondly, we propose a novel self-supervised FSS framework that does not rely on any annotation. Instead, it adaptively estimates the query mask by leveraging the eigenvectors obtained from the support images. This approach eliminates the need for manual annotation, making it particularly suitable for medical images with limited annotated data. Thirdly, to further enhance the decoding of the query image based on the information provided by the support image, we introduce a multi-scale large kernel attention module. By selectively emphasizing relevant features and details, this module improves the segmentation process and contributes to better object delineation. Evaluations on both natural and medical image datasets demonstrate the efficiency and effectiveness of our method. Moreover, the proposed approach is characterized by its generality and model-agnostic nature, allowing for seamless integration with various deep architectures. The code is publicly available at \href{https://github.com/mindflow-institue/annotation_free_fewshot}{\textcolor{magenta}{GitHub}}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
