Montana, Giovanni

Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker

Cole, J. H., Poudel, R. P. K., Tsagkrasoulis, D., Caan, M. W. A., Steves, C., Spector, T. D., & Montana, G. (2017) · NeuroImage

Dementia and Cognitive Impairment ResearchAdvanced MRI Techniques and Applications

Cited by 872

Deep neural networks for anatomical brain segmentation

de Brebisson, A., & Montana, G. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Medical Image Segmentation TechniquesBrain Tumor Detection and Classification

Cited by 330

Automated Triaging of Adult Chest Radiographs with Deep Artificial Neural Networks

Annarumma, M., Withey, S. J., Bakewell, R. J., Pesce, E., Goh, V., & Montana, G. (2019) · Radiology

Artificial Intelligence in Healthcare and EducationCOVID-19 diagnosis using AI

Cited by 303

False positives in neuroimaging genetics using voxel-based morphometry data

Silver, M., Montana, G., & Nichols, T. E. (2011) · NeuroImage

Functional Brain Connectivity StudiesGenetic Associations and Epidemiology

Cited by 146

Development and validation of open-source deep neural networks for comprehensive chest x-ray reading: a retrospective, multicentre study

Cid, Y. D., Macpherson, M., Gervais-Andre, L., Zhu, Y., Franco, G., Santeramo, R., Lim, C., Selby, I., Muthuswamy, K., Amlani, A., et al. (2024) · The Lancet Digital Health

Artificial Intelligence in Healthcare and EducationCOVID-19 diagnosis using AI

Cited by 37

Possible relationship between common genetic variation and white matter development in a pilot study of preterm infants

Krishnan, M. L., Wang, Z., Silver, M., Boardman, J. P., Ball, G., Counsell, S. J., Walley, A. J., Montana, G., & Edwards, A. D. (2016) · Brain and Behavior

Neonatal and fetal brain pathologyInfant Development and Preterm Care

Cited by 28