Evaluation of the quality and the productivity of neuroradiological reading of multiple sclerosis follow-up MRI scans using an intelligent automation software

Neuroradiology - 2024 66, 361–369

​

Improved Detection, Description and Efficiency of Multiple Sclerosis Neuroradiological Lesions’ Assessment using a Semi-Automatic Dedicated Deep-Learning Based Software Jazz

medRxiv 2022.06.22.22276781

​

The R-AI-DIOLOGY checklist: a practical checklist for evaluation of artificial intelligence tools in clinical neuroradiology

Neuroradiology - 2022 May;64(5):851-864

​​

Editorial: Machine Learning in Neuroimaging

Frontiers in Neuroradiology - 2021, Nov 24;12:778765.

​

Realistic generation of diffusion-weighted magnetic resonance brain images with deep generative models

Magnetic Resonance Imaging - 2021 Sep;81:60-66​

​​​

Optimizing Deep Learning Algorithms for Segmentation of Acute Infarcts on Non-Contrast Material-enhanced CT Scans of the Brain Using Simulated Lesions

Radiology: Artificial Intelligence - 2021 May 12;3(4):e200127

​

Improved Segmentation and Detection Sensitivity of Diffusion-weighted Stroke Lesions with Synthetically Enhanced Deep Learning

Radiology: Artificial Intelligence - 2020 Sep 16;2(5):e190217

​

The Bright, Artificial Intelligence-Augmented Future of Neuroimaging Reading

Frontiers in Neuroradiology - 2017 Sep 21;8:489