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

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

Radiol Artif Intell. 2020 Sep 16;2(5):e190217

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

Magn Reson Imaging. 2021 Sep;81:60-66

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

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

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Editorial: Machine Learning in Neuroimaging

Front Neurol. 2021 Nov 24;12:778765.

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

Radiol Artif Intell. 2021 May 12;3(4):e200127.

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The Bright, Artificial Intelligence-Augmented Future of Neuroimaging Reading

Front Neurol. 2017 Sep 21;8:489.