Graph neural networks (GNNs) present a promising alternative to CNNs and
transformers in certain image processing applications due to their
parameter-efficiency in modeling spatial relationships. Currently, a major area
of research involves the converting non-graph input data for GNN-based models,
notably in scenarios where the data originates from images. One approach
involves converting images into nodes by identifying significant keypoints
within them. Super-Retina, a semi-supervised technique, has been utilized for
detecting keypoints in retinal images. However, its limitations lie in the
dependency on a small initial set of ground truth keypoints, which is
progressively expanded to detect more keypoints. Having encountered
difficulties in detecting consistent initial keypoints in brain images using
SIFT and LoFTR, we proposed a new approach: radiomic feature-based keypoint
detection. Demonstrating the anatomical significance of the detected keypoints
was achieved by showcasing their efficacy in improving registration processes
guided by these keypoints. Subsequently, these keypoints were employed as the
ground truth for the keypoint detection method (LK-SuperRetina). Furthermore,
the study showcases the application of GNNs in image matching, highlighting
their superior performance in terms of both the number of good matches and
confidence scores. This research sets the stage for expanding GNN applications
into various other applications, including but not limited to image
classification, segmentation, and registration.

利用基于放射学特征的关键点检测方法，本研究证明了在图神经网络中的关键点在改进配准过程中的解剖学意义，并展示了图神经网络在图像匹配方面的卓越性能，为拓展图神经网络在图像分类、分割和配准等多个应用领域铺平了道路。

利用放射维度特征分析进行自动 MRI 关键点检测：优化图形应用

Utilizing Radiomic Feature Analysis For Automated MRI Keypoint  Detection: Enhancing Graph Applications

Graph neural networks (GNNs) demonstrate outstanding performance in a broad
range of applications. While the majority of GNN applications assume that a
graph structure is given, some recent methods substantially expanded the
applicability of GNNs by showing that they may be effective even when no graph
structure is explicitly provided. The GNN parameters and a graph structure are
jointly learned. Previous studies adopt different experimentation setups,
making it difficult to compare their merits. In this paper, we propose a
benchmarking strategy for graph structure learning using a unified framework.
Our framework, called Unified Graph Structure Learning (UGSL), reformulates
existing models into a single model. We implement a wide range of existing
models in our framework and conduct extensive analyses of the effectiveness of
different components in the framework. Our results provide a clear and concise
understanding of the different methods in this area as well as their strengths
and weaknesses. The benchmark code is available at
this https URL

我们提出了一种名为统一图结构学习（UGSL）的框架，用于图结构学习的基准策略，该框架将现有模型重构为一个单一模型，并对框架中不同组件的有效性进行了广泛分析，结果详细说明了该领域中的不同方法及其优缺点。