Výsledky vyhledávání - "Chanon Chantaduly"

Artificial Intelligence Assessment of Renal Scarring (AIRS Study)

Autor: Wei Ling Lau, Peter Chang, Karla A. Perez Reyes, Hayden R. Troutt, Chanon Chantaduly, Jonathan E. Zuckerman

Publikováno v: Kidney360, vol 3, iss 1
Kidney360

BACKGROUND: The goal of the Artificial Intelligence in Renal Scarring (AIRS) study is to develop machine learning tools for noninvasive quantification of kidney fibrosis from imaging scans. METHODS: We conducted a retrospective analysis of patients w

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a80aa824624e03a43909e9e3629e43e6
https://doi.org/10.34067/kid.0003662021

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A Semi-Supervised Contrastive Learning Approach to Alzheimer’s Disease Diagnostics using Convolutional Autoencoders

Autor: Edward Jung, Anshul Kashyap, Brandon Hsu, Mason Moreland, Chanon Chantaduly, Peter D. Chang

PURPOSEAlzheimer’s Disease (AD) is a neurodegenerative disease that progressively deteriorates memory and cognitive abilities. PET 18F-AV45 (florbetapir) is a common imaging modality used to characterize the distribution of beta-amyloid deposits in

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::3738936a99c988e11a2da5b566e1716d
https://doi.org/10.1101/2022.12.27.22283984

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Segmentation of the Prostate Transition Zone and Peripheral Zone on MR Images with Deep Learning

Autor: Chanon Chantaduly, Daniel S. Chow, Roozbeh Houshyar, Mark Rupasinghe, Alexander Ushinsky, Michelle Bardis, Peter Chang, Chantal Chahine, Karen Tran-Harding

Publikováno v: Radiol Imaging Cancer
Radiology. Imaging cancer, vol 3, iss 3

PURPOSE: To develop a deep learning model to delineate the transition zone (TZ) and peripheral zone (PZ) of the prostate on MR images. MATERIALS AND METHODS: This retrospective study was composed of patients who underwent a multiparametric prostate M

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::25dcd02440ce45dfbc04352dc3436c44
https://pubmed.ncbi.nlm.nih.gov/33929265

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A 3D-2D Hybrid U-Net Convolutional Neural Network Approach to Prostate Organ Segmentation of Multiparametric MRI

Autor: Chanon Chantaduly, Justin Glavis-Bloom, Daniel S. Chow, Alexander Ushinsky, Michelle Bardis, Michael Nguyentat, Edward Uchio, Peter Chang, Roozbeh Houshyar

Publikováno v: AJR. American journal of roentgenology. 216(1)

Prostate cancer is the most commonly diagnosed cancer in men in the United States with more than 200,000 new cases in 2018. Multiparametric MRI (mpMRI) is increasingly used for prostate cancer evaluation. Prostate organ segmentation is an essential s

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::815e30da68a2aeaa2ab1d290853bf250
https://pubmed.ncbi.nlm.nih.gov/32812797

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Deep Learning with Limited Data: Organ Segmentation Performance by U-Net

Autor: Daniel S. Chow, Madeleine Shaver, Michelle Bardis, Roozbeh Houshyar, Justin Glavis-Bloom, Edward Uchio, Alexander Ushinsky, Peter Chang, Chanon Chantaduly

Publikováno v: Electronics
Volume 9
Issue 8
Electronics, Vol 9, Iss 1199, p 1199 (2020)

(1) Background: The effectiveness of deep learning artificial intelligence depends on data availability, often requiring large volumes of data to effectively train an algorithm. However, few studies have explored the minimum number of images needed f

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fa08f0befe76900fe6826f77b248657f

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Optimizing Neuro-Oncology Imaging: A Review of Deep Learning Approaches for Glioma Imaging

Autor: Brent D. Weinberg, Madeleine Shaver, Chanon Chantaduly, Daniel S. Chow, Jack Grinband, Daniela A. Bota, Catherine Chiou, Christopher G. Filippi, Paul Kohanteb, Michelle Bardis, Peter Chang

Publikováno v: Cancers, Vol 11, Iss 6, p 829 (2019)
Cancers, vol 11, iss 6
Cancers

Radiographic assessment with magnetic resonance imaging (MRI) is widely used to characterize gliomas, which represent 80% of all primary malignant brain tumors. Unfortunately, glioma biology is marked by heterogeneous angiogenesis, cellular prolifera

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e7a83197abf2f0009ca1332e4c6d2bdf
https://www.mdpi.com/2072-6694/11/6/829

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Deep learning segmentation of kidneys with renal cell carcinoma

Autor: Roozbeh Houshyar, Daniel S. Chow, Chanon Chantaduly, Jeremy W. Martin, Alexander Ushinsky, Chengfeng Liu, Justin Glavis-Bloom, Michelle Bardis, Hannah Liu, Peter Chang, Kevin Yin

Publikováno v: Journal of Clinical Oncology. 37:e16098-e16098

e16098 Background: Renal cell carcinoma (RCC) is the most common kidney malignancy and the eighth most common cancer overall, with more than 60,000 new cases and nearly 15,000 deaths in the United States each year. The majority of RCC is asymptomatic

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::dcf78405dc9f185dc5d18bc9fd7e0757
https://doi.org/10.1200/jco.2019.37.15_suppl.e16098

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Automated prostate lesion detection and PI-RADS assessment with deep learning

Autor: Jasper Van, Justin Glavis-Bloom, Param Bhatter, Alexander Ushinsky, Daniel S. Chow, Roozbeh Houshyar, Michelle Bardis, Peter Chang, Chanon Chantaduly, Choongheon Yoon

Publikováno v: Journal of Clinical Oncology. 37:e16605-e16605

e16605 Background: Prostate Cancer is the most commonly diagnosed male cancer in the U.S. Multiparametric magnetic resonance imaging (mpMRI) is increasingly used for both prostate cancer evaluation and biopsy guidance. The PI-RADS v2 scoring paradigm

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::bd7425e73f9b5900003a1255a3fa053a
https://doi.org/10.1200/jco.2019.37.15_suppl.e16605

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