Graph neural networks have emerged as a promising approach for the analysis of non-Euclidean data such as meshes. In medical imaging, mesh-like data plays an important role for modelling anatomical structures, and shape classification can be used in computer aided diagnosis and disease detection. However, with a plethora of options, the best architectural choices for medical shape analysis using GNNs remain unclear. We conduct a comparative analysis to provide practitioners with an overview of the current state-of-the-art in geometric deep learning for shape classification in neuroimaging. Using biological sex classification as a proof-of-concept task, we find that using FPFH as node features substantially improves GNN performance and generalisation to out-of-distribution data; we compare the performance of three alternative convolutional layers; and we reinforce the importance of data augmentation for graph based learning. We then confirm these results hold for a clinically relevant task, using the classification of Alzheimer's disease.
Linear Algebraic Approaches to Neuroimaging Data Compression: A Comparative Analysis of Matrix and Tensor Decomposition Methods for High-Dimensional Medical Images
Comparative Study of Anatomical and Learned Features in AI Models for Structural Brain MRI
Center for Data Science, New York University（纽约大学数据科学中心）；NYU Grossman School of Medicine（纽约大学格罗斯曼医学院）；Courant Institute of Mathematical Sciences, NYU（纽约大学柯朗数学科学研究所）
Feature visualization for convolutional neural network models trained on neuroimaging data
Charité – Universitätsmedizin Berlin（柏林夏里特医学院）；Freie Universität Berlin（柏林自由大学）；Humboldt-Universität zu Berlin（柏林洪堡大学）；Berlin Institute of Health（柏林健康研究所）；Bernstein Center for Computational Neuroscience（伯恩斯坦计算神经科学中心）
Deep Learning for Brain Age Estimation: A Systematic Review
Indian Institute of Technology Indore（印度理工学院印多尔分校）；University of Michigan（密歇根大学）；University of Manitoba（曼尼托巴大学）；National Institute of Technology Silchar（锡尔查尔国家理工学院）；National Taipei University of Technology（国立台北科技大学）；Duke Kunshan University（昆山杜克大学）；University of Leicester（莱斯特大学）；TECNALIA（泰克纳利亚）；Basque Research and Technology Alliance (BRTA)（巴斯克研究与技术联盟）；University of the Basque Country (UPV/EHU)（巴斯克大学）
Review of Statistical Shape Spaces for 3D Data with Comparative Analysis for Human Faces
Fraunhofer Institute for Computer Graphics Research IGD（弗劳恩霍夫计算机图形学研究所IGD）；Instituto Tecnológico Metropolitano - ITM（大都会理工学院（ITM））；Saarland University（萨尔大学）
Feature, Alignment, and Supervision in Category Learning: A Comparative Approach with Children and Neural Networks
Department of Psychology, University of Southern California（南加州大学心理学系）；Department of Statistics and Data Science, University of California, Los Angeles（加州大学洛杉矶分校统计与数据科学系）
ASMCNN: An Efficient Brain Extraction Using Active Shape Model and Convolutional Neural Networks
Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Dublin City University（都柏林城市大学）；Dongguk University（东国大学）；University of Louisiana at Lafayette（路易斯安那大学拉斐特分校）；University of Oulu（奥卢大学）；University of Architecture of Ho Chi Minh City（胡志明市建筑大学）；Saigon University（西贡大学）；VNU-HCMUS（越南胡志明市国家大学自然科学大学）；Vietnam National University, Ho Chi Minh City（越南胡志明市国家大学）
