CoBooM：基于码本引导的自举方法用于医学图像表示学习
CoBooM: Codebook Guided Bootstrapping for Medical Image Representation Learning
AI总结：
CoBooM 提出一种通过码本引导结合连续与离散表示的自监督医学图像学习框架，利用解剖相似性提升特征表示能力，并在胸部 X 光片和眼底图像上取得分类与分割性能的显著提升。
摘要：
自监督学习（SSL）通过利用未标注数据，已成为医学图像分析中一种有前景的范式。尽管现有 SSL 方法具有潜力，但它们忽视了医学图像中固有的高度解剖相似性。这使得 SSL 方法难以一致地捕获医学图像中多样化的语义内容。本文提出了一种新颖且通用的解决方案，通过在 SSL 中集成码本来隐式利用解剖相似性。码本作为视觉模式的简洁且信息丰富的字典，不仅有助于捕获细微的解剖细节，还促进了鲁棒且泛化的特征表示的构建。在此背景下，我们提出了 CoBooM，这是一种通过集成连续表示和离散表示进行自监督医学图像学习的新框架。连续分量确保保留细粒度细节，而离散分量则通过结构化嵌入空间促进粗粒度特征提取。为了理解 CoBooM 的有效性，我们对涵盖胸部 X 光片和眼底图像的多种医学数据集进行了全面评估。实验结果表明，在分类和分割任务中性能均有显著提升。
英文摘要：
Self-supervised learning (SSL) has emerged as a promising paradigm for medical image analysis by harnessing unannotated data. Despite their potential, the existing SSL approaches overlook the high anatomical similarity inherent in medical images. This makes it challenging for SSL methods to capture diverse semantic content in medical images consistently. This work introduces a novel and generalized solution that implicitly exploits anatomical similarities by integrating codebooks in SSL. The codebook serves as a concise and informative dictionary of visual patterns, which not only aids in capturing nuanced anatomical details but also facilitates the creation of robust and generalized feature representations. In this context, we propose CoBooM, a novel framework for self-supervised medical image learning by integrating continuous and discrete representations. The continuous component ensures the preservation of fine-grained details, while the discrete aspect facilitates coarse-grained feature extraction through the structured embedding space. To understand the effectiveness of CoBooM, we conduct a comprehensive evaluation of various medical datasets encompassing chest X-rays and fundus images. The experimental results reveal a significant performance gain in classification and segmentation tasks.
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。
