M
Mert R. Sabuncu
Researcher at Cornell University
Publications - 148
Citations - 12746
Mert R. Sabuncu is an academic researcher from Cornell University. The author has contributed to research in topics: Computer science & Convolutional neural network. The author has an hindex of 24, co-authored 118 publications receiving 9297 citations. Previous affiliations of Mert R. Sabuncu include Massachusetts Institute of Technology & Harvard University.
Papers
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Journal ArticleDOI
The organization of the human cerebral cortex estimated by intrinsic functional connectivity
B.T. Thomas Yeo,Fenna M. Krienen,Jorge Sepulcre,Jorge Sepulcre,Mert R. Sabuncu,Mert R. Sabuncu,Danial Lashkari,Marisa O. Hollinshead,Marisa O. Hollinshead,Joshua L. Roffman,Jordan W. Smoller,Lilla Zöllei,Jonathan R. Polimeni,Bruce Fischl,Bruce Fischl,Hesheng Liu,Randy L. Buckner +16 more
TL;DR: In this paper, the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI data from 1,000 subjects and a clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex.
Journal ArticleDOI
VoxelMorph: A Learning Framework for Deformable Medical Image Registration
TL;DR: VoxelMorph promises to speed up medical image analysis and processing pipelines while facilitating novel directions in learning-based registration and its applications and demonstrates that the unsupervised model’s accuracy is comparable to the state-of-the-art methods while operating orders of magnitude faster.
Proceedings Article
Generalized cross entropy loss for training deep neural networks with noisy labels
Zhilu Zhang,Mert R. Sabuncu +1 more
TL;DR: In this paper, a theoretically grounded set of noise-robust loss functions that can be seen as a generalization of mean absolute error (MAE) and categorical cross entropy (CCE) loss is proposed.
Proceedings ArticleDOI
An Unsupervised Learning Model for Deformable Medical Image Registration
TL;DR: The proposed method uses a spatial transform layer to reconstruct one image from another while imposing smoothness constraints on the registration field, and demonstrates registration accuracy comparable to state-of-the-art 3D image registration, while operating orders of magnitude faster in practice.
Journal ArticleDOI
VoxelMorph: A Learning Framework for Deformable Medical Image Registration
TL;DR: Zhou et al. as mentioned in this paper proposed VoxelMorph, a fast learning-based framework for deformable, pairwise medical image registration, which parameterizes the function via a convolutional neural network and optimizes the parameters of the neural network on a set of images.