Deep learning for brain disorders: from data processing to disease treatment

Ninon Burgos (1) , Simona Bottani (1) , Johann Faouzi (1) , Elina Thibeau-Sutre (1) , Olivier Colliot (1)

1 ARAMIS - Algorithms, models and methods for images and signals of the human brain = Algorithmes, modèles et méthodes pour les images et les signaux du cerveau humain [ICM Paris]

In order to reach precision medicine and improve patients' quality of life, machine learning is increasingly used in medicine. Brain disorders are often complex and heterogeneous, and several modalities such as demographic, clinical, imaging, genetics and environmental data have been studied to improve their understanding. Deep learning, a subpart of machine learning, provides complex algorithms that can learn from such various data. It has become state of the art in numerous fields, including computer vision and natural language processing, and is also growingly applied in medicine. In this article, we review the use of deep learning for brain disorders. More specifically, we identify the main applications, the concerned disorders and the types of architectures and data used. Finally, we provide guidelines to bridge the gap between research studies and clinical routine.

Mots clés: Deep learning; Genomics; Medical imaging; Neurology

Domaines: Neurobiologie; Imagerie; Traitement des images [eess.IV]; Imagerie médicale; Vision par ordinateur et reconnaissance de formes [cs.CV]; Traitement du signal et de l'image [eess.SP]

Ninon Burgos, Simona Bottani, Johann Faouzi, Elina Thibeau-Sutre, Olivier Colliot. Deep learning for brain disorders: from data processing to disease treatment. Briefings in Bioinformatics, 2021, 22 (2), pp.1560-1576. ⟨10.1093/bib/bbaa310⟩. ⟨hal-03070554⟩