Ariel Rokem

The paper combines citizen science and deep learning to scale expert decision making in neuroimaging by amplifying labels and training models to emulate expert quality control.

This paper uses deep learning to generate perfusion maps from structural OCT images without expert labels, outperforming clinicians in fidelity.

This study trains a deep learning model to predict future 24-2 Humphrey Visual Fields up to 5.5 years ahead using historical data from glaucomatous patients.

This study applies biophysical models to diffusion MRI data from children to assess white matter properties and their relationship with reading skill.

This study uses a computational model to recommend optimal electrode placement for retinal implants to reduce axonal activation and improve visual perception.

The paper tracks anatomical changes in white matter during reading instruction and finds that some tracts predict reading skill while others change with experience.

This paper develops a neural network for multiclass and multilabel classification of OCT retinal images, incorporating patient age, gender, and visual acuity to improve diagnosis.

MRI2MRI transforms between brain MRI contrasts using a deep convolutional network, improving spatial registration across different imaging modalities.

AFQ-Browser provides a browser-based tool for visualizing diffusion MRI data to enhance reproducibility and data sharing in human neuroscience research.

This study shows that moderate data (1,800 patients) can achieve near-maximal performance in AMD classification using OCT images, and that separate test sets are not always necessary.

pulse2percept simulates the perceptual experience of retinal prosthesis patients to aid in the development of visual prostheses.

The paper develops a CNN to segment macular edema in OCT images, achieving performance comparable to clinicians.

This paper reviews the role of cortical plasticity in visual prosthetics, discussing how biological constraints affect the ability to restore functional vision through retinal implants.

This paper provides an open-source implementation of a two-compartment free water elimination model for diffusion tensor imaging and evaluates its performance using simulations.

This review discusses diffusion MRI and fiber tractography methods for studying human visual white matter connections and their properties.

The paper investigates how voluntary visual-spatial attention affects orientation-specific surround suppression in contrast discrimination tasks, finding that attention influences perceptual sensitivity but not the baseline contrast perception.

The paper introduces BIDS, a standard for organizing MRI datasets to improve data sharing and processing.