Daniel Tward | Classical and Deep Learning Approaches to Longitudinal Modeling in ... | CGSI 2024 скачать в хорошем качестве

Daniel Tward | Classical and Deep Learning Approaches to Longitudinal Modeling in ... | CGSI 2024

Daniel Tward | Classical and Deep Learning Approaches to Longitudinal Modeling in Neuroimages | CGSI 2024 Related Papers: Tward, D. J., Sicat, C. S., Brown, T., Bakker, A., Gallagher, M., Albert, M., ... & Alzheimer's Disease Neuroimaging Initiative. (2017). Entorhinal and transentorhinal atrophy in mild cognitive impairment using longitudinal diffeomorphometry. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, 9, 41-50. Sauty, B., & Durrleman, S. (2022, September). Progression models for imaging data with longitudinal variational auto encoders. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 3-13). Cham: Springer Nature Switzerland. Ravi, D., Alexander, D. C., Oxtoby, N. P., & Alzheimer’s Disease Neuroimaging Initiative. (2019, October). Degenerative adversarial neuroimage nets: generating images that mimic disease progression. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 164-172). Cham: Springer International Publishing.