标题： iCBIR-Sli：具有二维切片嵌入的可解释基于内容的图像检索 显示英文标题

标题： iCBIR-Sli: Interpretable Content-Based Image Retrieval with 2D Slice Embeddings

摘要： 当前用于搜索脑部MRI图像的方法依赖于基于文本的方法，这凸显了对基于内容的图像检索（CBIR）系统的重要需求。 直接将3D脑部MRI图像应用于机器学习模型可以有效学习大脑结构；然而，构建通用模型需要大量训练数据。 虽然考虑深度方向并利用连续2D切片的模型在涉及3D数据的分割和分类任务中已显示出成功，但仍存在一些担忧。 具体而言，使用一般的2D切片可能导致忽略深度方向信息中的病理特征和不连续性。 此外，据作者所知，尚未有尝试开发一种能够保留整个大脑结构信息的实际CBIR系统。 在本研究中，我们提出了一种可解释的脑部MRI图像CBIR方法，命名为iCBIR-Sli（基于2D切片嵌入的可解释CBIR），这是全球首次利用一系列2D切片的方法。 iCBIR-Sli通过有效聚合切片信息来解决使用2D切片相关的问题，从而实现了具有高完整性、可用性、鲁棒性和互操作性的低维表示，这些特性对于有效的CBIR至关重要。 在使用五个公开可用的脑部MRI数据集（ADNI2/3，OASIS3/4，AIBL）进行检索评估实验中，针对阿尔茨海默病和认知正常的病例，iCBIR-Sli表现出顶级的检索性能（宏F1=0.859），与专门设计用于分类的现有深度学习模型相当，而无需外部分类器。 此外，该方法通过明确识别与所搜索疾病相关的脑区，提供了高度的可解释性。 显示英文摘要

摘要： Current methods for searching brain MR images rely on text-based approaches, highlighting a significant need for content-based image retrieval (CBIR) systems. Directly applying 3D brain MR images to machine learning models offers the benefit of effectively learning the brain's structure; however, building the generalized model necessitates a large amount of training data. While models that consider depth direction and utilize continuous 2D slices have demonstrated success in segmentation and classification tasks involving 3D data, concerns remain. Specifically, using general 2D slices may lead to the oversight of pathological features and discontinuities in depth direction information. Furthermore, to the best of the authors' knowledge, there have been no attempts to develop a practical CBIR system that preserves the entire brain's structural information. In this study, we propose an interpretable CBIR method for brain MR images, named iCBIR-Sli (Interpretable CBIR with 2D Slice Embedding), which, for the first time globally, utilizes a series of 2D slices. iCBIR-Sli addresses the challenges associated with using 2D slices by effectively aggregating slice information, thereby achieving low-dimensional representations with high completeness, usability, robustness, and interoperability, which are qualities essential for effective CBIR. In retrieval evaluation experiments utilizing five publicly available brain MR datasets (ADNI2/3, OASIS3/4, AIBL) for Alzheimer's disease and cognitively normal, iCBIR-Sli demonstrated top-1 retrieval performance (macro F1 = 0.859), comparable to existing deep learning models explicitly designed for classification, without the need for an external classifier. Additionally, the method provided high interpretability by clearly identifying the brain regions indicative of the searched-for disease.