[HTML][HTML] Overcoming the challenges in the development and implementation of artificial intelligence in radiology: a comprehensive review of solutions beyond …

GS Hong, M Jang, S Kyung, K Cho… - Korean Journal of …, 2023 - ncbi.nlm.nih.gov
Artificial intelligence (AI) in radiology is a rapidly developing field with several prospective
clinical studies demonstrating its benefits in clinical practice. In 2022, the Korean Society of …

A review of causality for learning algorithms in medical image analysis

A Vlontzos, D Rueckert, B Kainz - arXiv preprint arXiv:2206.05498, 2022 - arxiv.org
Medical image analysis is a vibrant research area that offers doctors and medical
practitioners invaluable insight and the ability to accurately diagnose and monitor disease …

[HTML][HTML] Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3d deep generative models

G Pombo, R Gray, MJ Cardoso, S Ourselin, G Rees… - Medical Image …, 2023 - Elsevier
We describe CounterSynth, a conditional generative model of diffeomorphic deformations
that induce label-driven, biologically plausible changes in volumetric brain images. The …

[HTML][HTML] Minimum detectable spinal cord atrophy with automatic segmentation: Investigations using an open-access dataset of healthy participants

P Bautin, J Cohen-Adad - NeuroImage: Clinical, 2021 - Elsevier
Spinal cord atrophy is a well-known biomarker in multiple sclerosis (MS) and other diseases.
It is measured by segmenting the spinal cord on an MRI image and computing the average …

Causal reasoning in medical imaging

A Vlontzos, C Müller, B Kainz - Trustworthy AI in Medical Imaging, 2025 - Elsevier
Medical image analysis is a vibrant research area that offers doctors and medical
practitioners valuable insight and the ability to accurately diagnose and monitor disease …