Amir Bar

I am an AI Research Lead at Zebra Medical, where I lead the research and development of clinical algorithms for the detection of acute findings in CT scans. Our team is based in Berkeley, California.

My research interests span from vision to language and I enjoy tackling large scale cross-modal problems in efficient ways. I graduated from Tel Aviv University, with a Master's degree in Computer Science. I was a member of the TAU Deep Learning lab, where I was advised by Lior Wolf.

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Automated opportunistic osteoporotic fracture risk assessment using computed tomography scans to aid in FRAX underutilization    
Noa Dagan, Eldad Elnekave, Noam Barda, Orna Bregman-Amitai, Amir Bar, Mila Orlovsky, Eitan Bachmat & Ran D. Balicer
Nature Medicine, 2020
Press 1 2

Methods for identifying patients at high risk for osteoporotic fractures are underutilized. We demonstrate it is feasibile to automatically evaluate risk based on routine abdomen or chest computed tomography (CT) scan.

Learning Individual Styles of Conversational Gesture  
Shiry Ginosar*, Amir Bar*, Gefen Kohavi, Caroline Chan, Andrew Owens, Jitendra Malik
CVPR, 2019
Press | Project Page | Code | Data

We predict plausible gestures to go along with someone's speech.

Improved ICH classification using task-dependent learning  
Amir Bar, Michal Mauda-Havakuk, Yoni Turner, Michal Safadi, Eldad Elnekave
ISBI, 2019

Intracranial hemorrhage (ICH) is among the most critical and timesensitive findings to be detected on Head CT. We present a new architecture designed for optimal triaging of Head CTs, with the goal of decreasing the time from CT acquisition to accurate ICH detection. These results are comparable to previously reported results with smaller number of tagged studies.

Simulating Dual-Energy X-Ray Absorptiometry in CT Using Deep-Learning Segmentation Cascade
Arun Krishnaraj, Spencer Barrett, Orna Bregman-Amitai , Michael Cohen-Sfady, Amir Bar, David Chettrit, Mila Orlovsky, Eldad Elnekave
Journal of the American College of Radiology, 2019

Osteoporosis is an underdiagnosed condition despite effective screening modalities. The purpose of this study was to describe a method to simulate lumbar DEXA scores from routinely acquired CT studies using a machine-learning algorithm.

PHT-bot: a deep learning based system for automatic risk stratification of COPD patients based upon signs of pulmonary hypertension
David Chettrit, Orna Bregman Amitai, Itamar Tamir, Amir Bar and Eldad Elnekave
SPIE, 2019

Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of morbidity and mortality worldwide. We apply deep learning algorithm to detect those at risk.


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Language Generation with Recurrent Generative Adversarial Networks without Pre-training
Ofir Press*, Amir Bar*, Ben Bogin*, Jonathan Berant, Lior Wolf
1st Workshop on Learning to Generate Natural Language at ICML, 2017

We show that recurrent neural networks can be trained to generate text with GANs from scratch and vastly improve the quality of generated sequences compared to a convolutional baseline.

Compression Fractures Detection on CT
Amir Bar, Lior Wolf, Orna Bregman Amitai, Eyal Toledano, Eldad Elnekave
SPIE, 2017
Press 1 2

The presence of a vertebral compression fracture is highly indicative of osteoporosis and represents the single most robust predictor for development of a second osteoporotic fracture in the spine or elsewhere. We present an automated method for detecting spine compression fractures in Computed Tomography (CT) scans.


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