Graph Foundation Model for Medical Image Analysis
The integration of diverse medical imaging modalities, such as MRI, CT, PET, and histopathological images, presents significant opportunities for advancing precision medicine, diagnostics, and treatment strategies. However, the complexity of relationships within this imaging data poses unique challenges in data representation, fusion, and analysis. Graph foundation models, a powerful tool for capturing relationships and dependencies between imaging entities, are uniquely suited to address these challenges, especially in the context of medical imaging where connections between different types of images are crucial for holistic medical insights.This special issue is necessary to bring together the latest research contributions from both academia and industry, focusing specifically on the application of graph foundation models in medical image analysis. Unlike other general calls for machine learning or AI in healthcare, this special issue will provide a focused platform for exploring how graph foundation models can be harnessed to solve the unique problems associated with medical imaging data. The goal is to advance both the theoretical foundations and practical implementations of graph-based models in healthcare, bridging the gap between AI research and real-world medical applications.
The special issue welcomes research contributions related to the following topics:Multimodal Integration Using Graph Foundation Models
Graph Foundation Models for Segmentation, Detection, and Classification
Explainable and Interpretable Graph Foundation Models
Graph-Based Generative Models for Medical Image Synthesis
Real-Time Graph Foundation Models in Clinical Practice
Graph Foundation Models in Radiomics and Radiogenomics
Clinical Applications of Graph Foundation Models for Personalized Medicine
Guest editors:
Yue Gao, PhD
Tsinghua University, Beijing, China
gaoyue@tsinghua.edu.cn
Angelica I Aviles-Rivero, PhD
University of Cambridge, Cambridge, UK
ai323@cam.ac.uk
Mingxia Liu, PhD
University of North Carolina at Chapel Hill, North Carolina, USA
mingxia_liu@med.unc.edu
Manuscript submission information:
The journal submission system (https://www.editorialmanager.com/pr/default.aspx) will be open for submissions to our Special Issue from October 15, 2024. When submitting your manuscript please select the article type VSI: Graph Foundation Model. Both the Guide for Authors and the submission portal could be found on the Journal Homepage: https://www.sciencedirect.com/journal/pattern-recognition/publish/guide-for-authors.
Submission Portal Open: October 15, 2024
Submission Deadline: February 01, 2025
Keywords:
Foundation Model, Graph Neural Networks, Hypergraph Neural Networks, Medical Image Analysis
Pattern Recognition的CAR指数
2023年3月份科睿唯安官方一次性踢除35本SCI期刊，多数涉及学术诚信问题，让我们意识到学术期刊的“被踢”指数，也很重要。目前，对于期刊的“被踢”指数，这里介绍一下：CAR指数（关于CAR的详细介绍，请关注：www.jcarindex.com），这是一种评价期刊学术诚信风险的指数，指数越高代表可能的风险越大。从数据看，Pattern Recognition不管是2022年度，还是2023年度的CAR指数，都是比较低的。当然，CAR指数仅供参考，期刊风险情况，需以科睿唯安或中科院预警等官方为准！
让推送更美好~
