Guest Editorial Special Issue on Geometric Deep Learning in Medical Imaging
Abstract
In recent years, more and more attention has been devoted to geometric deep learning (GDL) and its applications to various problems in medical imaging. Unlike convolutional neural networks (CNNs) limited to 2-D/3-D grid-structured data, GDL can handle non-Euclidean data (i.e., graphs and manifolds) and is hence well-suited for medical imaging data such as structure-function connectivity networks, imaging genetics and omics, spatio-temporal anatomical representations, physics-informed GDL for optimal imaging sampling and acquisition, GDL in imaging inverse problems, etc. However, despite recent advances in GDL research, questions remain on how best to learn representations of non-Euclidean medical imaging data; how to convolve effectively on graphs; how to perform graph pooling/unpooling; how to handle heterogeneous data; and how to improve the interpretability of GDL. After discussing many other domain experts, we identify the need for a special issue that brings to the attention of the medical imaging community these interesting topics.
Read the paper
Related studies
- Learning from First-Generation Qualitative Approaches in the IS Discipline: An Evolutionary View and Some Implications for Authors and Evaluators (PART 1/2)
- Commentary on the clinical and preclinical dosage limits of interstitially administered magnetic fluids for therapeutic hyperthermia based on current practice and efficacy models
- Prosthesis: New Technological Opportunities and Innovative Biomedical Devices
- The history and future of neural modeling for cochlear implants
- Ryanodine receptors: waking up from refractoriness
- Novel Transgenic Rabbit Model Sheds Light on the Puzzling Role of Matrix Metalloproteinase-12 in Atherosclerosis
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.