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Russell T. Shinohara

Researcher at University of Pennsylvania

Publications -  358
Citations -  13915

Russell T. Shinohara 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 44, co-authored 267 publications receiving 8301 citations. Previous affiliations of Russell T. Shinohara include Henry M. Jackson Foundation for the Advancement of Military Medicine & Johns Hopkins University.

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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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Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity.

Rastko Ciric, +13 more
- 01 Jul 2017 - 
TL;DR: A systematic evaluation of 14 participant‐level confound regression methods for functional connectivity highlights the heterogeneous efficacy of existing methods, and suggests that different confounding regression strategies may be appropriate in the context of specific scientific goals.
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Harmonization of cortical thickness measurements across scanners and sites.

Jean-Philippe Fortin, +14 more
- 15 Feb 2018 - 
TL;DR: It is shown that ComBat removes unwanted sources of scan variability while simultaneously increasing the power and reproducibility of subsequent statistical analyses, and is useful for combining imaging data with the goal of studying life‐span trajectories in the brain.
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Harmonization of multi-site diffusion tensor imaging data.

Jean-Philippe Fortin, +12 more
- 01 Nov 2017 - 
TL;DR: It is shown that the DTI measurements are highly site‐specific, highlighting the need of correcting for site effects before performing downstream statistical analyses, and that ComBat, a popular batch‐effect correction tool used in genomics, performs best at modeling and removing the unwanted inter‐site variability in FA and MD maps.
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On testing for spatial correspondence between maps of human brain structure and function.

Aaron Alexander-Bloch, +7 more
- 01 Sep 2018 - 
TL;DR: This paper addresses the correspondence problem using a spatial permutation framework to generate null models of overlap by applying random rotations to spherical representations of the cortical surface, an approach for which the approach constitutes a useful advance over widely‐used methods for the comparison of cortical maps.