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Christos Davatzikos

Researcher at University of Pennsylvania

Publications -  802
Citations -  51845

Christos Davatzikos is an academic researcher from University of Pennsylvania. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 102, co-authored 690 publications receiving 41056 citations. Previous affiliations of Christos Davatzikos include Hospital of the University of Pennsylvania & Johns Hopkins University.

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Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features

TL;DR: This set of labels and features should enable direct utilization of the TCGA/TCIA glioma collections towards repeatable, reproducible and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments, as well as performance evaluation of computer-aided segmentation methods.
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The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

Alex Zwanenburg, +70 more
- 01 May 2020 - 
TL;DR: A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software and could be excellently reproduced.
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Deformable Medical Image Registration: A Survey

TL;DR: This paper attempts to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain, and provides an extensive account of registration techniques in a systematic manner.
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Longitudinal Magnetic Resonance Imaging Studies of Older Adults: A Shrinking Brain

TL;DR: MRI scans of 92 nondemented older adults in the Baltimore Longitudinal Study of Aging provide essential information on the rate and regional pattern of age-associated changes against which pathology can be evaluated and suggest slower rates of brain atrophy in individuals who remain medically and cognitively healthy.
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
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.