Transfer Learning for Task Adaptation of Brain Lesion Assessment and Prediction of Brain Abnormalities Progression/Regression using Irregularity Age Map in Brain MRI
暂无分享,去创建一个
Taku Komura | Muhammad Febrian Rachmadi | Maria del C. Valdés Hernández | T. Komura | M. F. Rachmadi | M. Hernández
[1] Martin Styner,et al. Metamorphic Geodesic Regression , 2012, MICCAI.
[2] S. Allassonnière,et al. Using longitudinal metamorphosis to examine ischemic stroke lesion dynamics on perfusion-weighted images and in relation to final outcome on T2-w images , 2014, NeuroImage: Clinical.
[3] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[4] Nico Karssemeijer,et al. Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation , 2017, MICCAI.
[5] D. Rueckert,et al. White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks , 2017, NeuroImage: Clinical.
[6] Nick C Fox,et al. The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods , 2008, Journal of magnetic resonance imaging : JMRI.
[7] Isabelle Bloch,et al. From neonatal to adult brain MR image segmentation in a few seconds using 3D-like fully convolutional network and transfer learning , 2017, 2017 IEEE International Conference on Image Processing (ICIP).
[8] Taku Komura,et al. Segmentation of white matter hyperintensities using convolutional neural networks with global spatial information in routine clinical brain MRI with none or mild vascular pathology , 2018, Comput. Medical Imaging Graph..
[9] Daniel Rueckert,et al. Limited One-time Sampling Irregularity Age Map (LOTS-IAM): Automatic Unsupervised Detection of Brain White Matter Abnormalities in Structural Magnetic Resonance Images , 2018, bioRxiv.
[10] C. Jack,et al. Alzheimer's Disease Neuroimaging Initiative , 2008 .
[11] Marleen de Bruijne,et al. Transfer Learning Improves Supervised Image Segmentation Across Imaging Protocols , 2015, IEEE Trans. Medical Imaging.