2.2k citations · 5.8k across the 101 of their papers we have counts for
13 papers · 1 filter
Machine learning in resting-state fMRI analysis
Meenakshi Khosla, Keith Jamison, Gia H. Ngo +2
Machine learning techniques have gained prominence for the analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview of various u…
A Convolutional Autoencoder Approach to Learn Volumetric Shape Representations for Brain Structures
Evan M. Yu, Mert R. Sabuncu
We propose a novel machine learning strategy for studying neuroanatomical shape variation. Our model works with volumetric binary segmentation images, and requires no pre-processin…
Ensemble learning with 3D convolutional neural networks for connectome-based prediction
Meenakshi Khosla, Keith Jamison, Amy Kuceyeski +1
The specificty and sensitivity of resting state functional MRI (rs-fMRI) measurements depend on pre-processing choices, such as the parcellation scheme used to define regions of in…
VoxelMorph: A Learning Framework for Deformable Medical Image Registration
Guha Balakrishnan, Amy Zhao, Mert R. Sabuncu +2
We present VoxelMorph, a fast learning-based framework for deformable, pairwise medical image registration. Traditional registration methods optimize an objective function for each…
Medical Image Imputation from Image Collections
Adrian V. Dalca, Katherine L. Bouman, William T. Freeman +3
We present an algorithm for creating high resolution anatomically plausible images consistent with acquired clinical brain MRI scans with large inter-slice spacing. Although large…
3D Convolutional Neural Networks for Classification of Functional Connectomes
Meenakshi Khosla, Keith Jamison, Amy Kuceyeski +1
Resting-state functional MRI (rs-fMRI) scans hold the potential to serve as a diagnostic or prognostic tool for a wide variety of conditions, such as autism, Alzheimer's disease, a…
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
Zhilu Zhang, Mert R. Sabuncu
Deep neural networks (DNNs) have achieved tremendous success in a variety of applications across many disciplines. Yet, their superior performance comes with the expensive cost of…
Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration
Adrian V. Dalca, Guha Balakrishnan, John Guttag +1
Traditional deformable registration techniques achieve impressive results and offer a rigorous theoretical treatment, but are computationally intensive since they solve an optimiza…
Conditional Entropy as a Supervised Primitive Segmentation Loss Function
Sundaresh Ram, Mert R. Sabuncu
Supervised image segmentation assigns image voxels to a set of labels, as defined by a specific labeling protocol. In this paper, we decompose segmentation into two steps. The firs…
Joint Cell Nuclei Detection and Segmentation in Microscopy Images Using 3D Convolutional Networks
Sundaresh Ram, Vicky T. Nguyen, Kirsten H. Limesand +1
We propose a 3D convolutional neural network to simultaneously segment and detect cell nuclei in confocal microscopy images. Mirroring the co-dependency of these tasks, our propose…
A Probabilistic Disease Progression Model for Predicting Future Clinical Outcome
Yingying Zhu, Mert R. Sabuncu
In this work, we consider the problem of predicting the course of a progressive disease, such as cancer or Alzheimer's. Progressive diseases often start with mild symptoms that mig…
An Unsupervised Learning Model for Deformable Medical Image Registration
Guha Balakrishnan, Amy Zhao, Mert R. Sabuncu +2
We present a fast learning-based algorithm for deformable, pairwise 3D medical image registration. Current registration methods optimize an objective function independently for eac…
Deep convolutional neural networks for segmenting 3D in vivo multiphoton images of vasculature in Alzheimer disease mouse models
Mohammad Haft-Javaherian, Linjing Fang, Victorine Muse +3
The health and function of tissue rely on its vasculature network to provide reliable blood perfusion. Volumetric imaging approaches, such as multiphoton microscopy, are able to ge…