Fatemeh Zabihollahy - Dissemin

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All papers authored by Fatemeh Zabihollahy

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2024 Fatemeh Zabihollahy

ORCID

, Qi Miao

ORCID

, Sohaib Naim, Ida Sonni, Sitaram Vangala, Harrison Kim, William Hsu, Anthony Sisk, Robert Reiter, Steven S. Raman, Kyunghyun Sung

ORCID

Investigating MRI‐Associated Biological Aspects of Racial Disparities in Prostate Cancer for African American and White Men

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Fatemeh Zabihollahy

ORCID

, Sohaib Naim, Pornphan Wibulpolprasert, Robert E. Reiter, Steven S. Raman, Kyunghyun Sung

ORCID

Understanding Spatial Correlation Between Multiparametric MRI Performance and Prostate Cancer

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This paper is made freely available by the publisher.

2020 Fatemeh Zabihollahy

ORCID

, S. Rajan, E. Ukwatta

Machine Learning-Based Segmentation of Left Ventricular Myocardial Fibrosis from Magnetic Resonance Imaging

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This paper is made freely available by the publisher.

Nicola Schieda

ORCID

, Christopher S. Lim

ORCID

, Fatemeh Zabihollahy

ORCID

, Jorge Abreu‐Gomez, Satheesh Krishna

ORCID

ORCID

, Gerd Melkus, Eran Ukwatta, Baris Turkbey

ORCID

Quantitative Prostate MRI

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This paper is made freely available by the publisher.

Fatemeh Zabihollahy

ORCID

ORCID

ORCID

Patch-Based Convolutional Neural Network for Differentiation of Cyst From Solid Renal Mass on Contrast-Enhanced Computed Tomography Images

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2019 Fatemeh Zabihollahy

ORCID

, Eranga Ukwatta, Satheesh Krishna

ORCID

, Nicola Schieda

Fully automated localization of prostate peripheral zone tumors on apparent diffusion coefficient map MR images using an ensemble learning method

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This paper is made freely available by the publisher.

Fatemeh Zabihollahy

ORCID

, Nicola Schieda, Satheesh Krishna Jeyaraj

ORCID

, Eranga Ukwatta

Automated segmentation of prostate zonal anatomy on T2‐weighted (T2W) and apparent diffusion coefficient ( ADC ) map MR images using U‐Nets

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This paper was not found in any repository, but could be made available legally by the author.

Fatemeh Zabihollahy

ORCID

, James A. White, Eranga Ukwatta

Convolutional neural network‐based approach for segmentation of left ventricle myocardial scar from 3D late gadolinium enhancement MR images

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