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Jayashree Kalpathy-Cramer

Researcher at Harvard University

Publications -  293
Citations -  20158

Jayashree Kalpathy-Cramer is an academic researcher from Harvard University. The author has contributed to research in topics: Retinopathy of prematurity & Deep learning. The author has an hindex of 48, co-authored 293 publications receiving 13811 citations. Previous affiliations of Jayashree Kalpathy-Cramer include Stanford University & SEMATECH.

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3D Slicer as an image computing platform for the Quantitative Imaging Network.

Andriy Fedorov, +14 more
- 01 Nov 2012 - 
TL;DR: An overview of 3D Slicer is presented as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications and the utility of the platform in the scope of QIN is illustrated.
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The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

Bjoern H. Menze, +67 more
- 01 Oct 2015 - 
TL;DR: The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) as mentioned in this paper was organized in conjunction with the MICCAI 2012 and 2013 conferences, and twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low and high grade glioma patients.
Posted ContentDOI

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Spyridon Bakas, +438 more
- 05 Nov 2018 - 
TL;DR: This study assesses the state-of-the-art machine learning methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018, and investigates the challenge of identifying the best ML algorithms for each of these tasks.
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Consensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trials

Benjamin M. Ellingson, +41 more
- 01 Sep 2015 - 
TL;DR: The current document outlines consensus recommendations for a standardized Brain Tumor Imaging Protocol (BTIP), along with the scientific and practical justifications for these recommendations, resulting from a series of discussions between various experts involved in aspects of neuro-oncology neuroimaging for clinical trials.
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Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Convolutional Neural Networks

James M. Brown, +11 more
- 01 Jul 2018 - 
TL;DR: This fully automated algorithm diagnosed plus disease in ROP with comparable or better accuracy than human experts, which has potential applications in disease detection, monitoring, and prognosis in infants at risk of ROP.