M Wahi-Anwar
University of California, Los Angeles
17 Papers
13 Citations
M Wahi-Anwar is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 3, co-authored 6 publications.
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Papers
Standardization in Quantitative Imaging: A Multicenter Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Data Sets
Michael F. McNitt-Gray,Sandy Napel,Akshay Jaggi,Sarah A. Mattonen,Sarah A. Mattonen,Lubomir M. Hadjiiski,Mark Muzi,Dmitry B. Goldgof,Yoganand Balagurunathan,Larry Pierce,Paul E. Kinahan,Ella F. Jones,A. Nguyen,A. Virkud,Heang Ping Chan,Nastaran Emaminejad,M Wahi-Anwar,M. Daly,Mahmoud A. Abdalah,Hao Yang,Lin Lu,Wenbing Lv,Arman Rahmim,Aimilia Gastounioti,Sarthak Pati,Spyridon Bakas,Despina Kontos,Binsheng Zhao,Jayashree Kalpathy-Cramer,Keyvan Farahani +29 more
- 01 Jun 2020
TL;DR: Assessment of radiomic features when computed by several groups by using different software packages under very tightly controlled conditions highlights the value of feature definition standardization as well as the need to further clarify definitions for some features.
Automated Endotracheal Tube Placement Check Using Semantically Embedded Deep Neural Networks.
Koon Wong,Liza Shrestha,M Wahi-Anwar,Morgan Daly,George Foster,Fereidoun Abtin,Kathleen Ruchalski,Jonathan G. Goldin,Dieter R. Enzmann +8 more
TL;DR: In this paper , an artificial intelligence (AI) system that assists in checking endotracheal tube (ETT) placement on chest X-ray (CXRs) and evaluate whether it can move into clinical validation as a quality improvement tool was developed.
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The effects of variations in parameters and algorithm choices on calculated radiomics feature values: Initial investigations and comparisons to feature variability across CT image acquisition conditions
Nastaran Emaminejad,M Wahi-Anwar,John M. Hoffman,Grace Kim,Matthew S. Brown,Michael F. McNitt-Gray +5 more
- 02 Mar 2018
TL;DR: Results indicate the need for harmonization of feature calculations and identification of optimum parameters and algorithms in a radiomics study, and suggest a lack of standardization in radiomic feature extraction.
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TH-AB-207A-05: A Fully-Automated Pipeline for Generating CT Images Across a Range of Doses and Reconstruction Methods
TL;DR: The automated research pipeline promises to be a useful tool for either training or evaluating performance of quantitative imaging software such as classifiers and CAD algorithms across the range of acquisition and reconstruction parameters present in the clinical environment.
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SimpleMind adds thinking to deep neural networks
Young Jin Choi,M Wahi-Anwar +1 more
TL;DR: In this paper , the authors propose a solution to solve the problem of the problem: this paper ] of "uniformity" and "uncertainty" of the solution.
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