Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Seyedehnafiseh Mirniaharikandehei"'
Autor:
Seyedehnafiseh Mirniaharikandehei, Alireza Abdihamzehkolaei, Angel Choquehuanca, Marco Aedo, Wilmer Pacheco, Laura Estacio, Victor Cahui, Luis Huallpa, Kevin Quiñonez, Valeria Calderón, Ana Maria Gutierrez, Ana Vargas, Dery Gamero, Eveling Castro-Gutierrez, Yuchen Qiu, Bin Zheng, Javier A. Jo
Publikováno v:
Bioengineering, Vol 10, Iss 3, p 321 (2023)
Objective: To help improve radiologists’ efficacy of disease diagnosis in reading computed tomography (CT) images, this study aims to investigate the feasibility of applying a modified deep learning (DL) method as a new strategy to automatically se
Externí odkaz:
https://doaj.org/article/8ff902960d9b4d34ab350f31ae08624c
Autor:
Bin Zheng, Yuchen Qiu, Faranak Aghaei, Seyedehnafiseh Mirniaharikandehei, Morteza Heidari, Gopichandh Danala
Publikováno v:
Visual Computing for Industry, Biomedicine, and Art, Vol 2, Iss 1, Pp 1-14 (2019)
Abstract In order to develop precision or personalized medicine, identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest recently. Most of thes
Externí odkaz:
https://doaj.org/article/267dcadf76a043bcbcb247eb8ddbf300
Autor:
Alireza Abdihamzehkolaei, Seyedehnafiseh Mirniaharikandehei, Angel Choquehuanca, Marco Aedo, Wilmer Pacheco, Laura Estacio, Victor Cahui, Luis Huallpa, Kevin Quiñonez, Valeria Calderón, Ana Maria Gutierrez, Ana Vargas, Dery Gamero, Eveling Castro-Gutierrez, Yuchen Qiu, Bin Zheng, Javier A. Jo
Publikováno v:
Medical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::10d96cce5c77b56ab2e13a341719a294
https://doi.org/10.1117/12.2654495
https://doi.org/10.1117/12.2654495
Autor:
Megan Buechel, Angeles Alvarez Secord, Danielle Enserro, David M. O'Malley, Michael J. Birrer, Bin Zheng, Hong Liu, Robert A. Burger, Robert S. Mannel, Mark F. Brady, Heidi J. Gray, Katrina Wade, Krishnansu S. Tewari, Seyedehnafiseh Mirniaharikandehei, Kathleen N. Moore, Andrew B. Nixon
Publikováno v:
Gynecol Oncol
Purpose Increasing measures of adiposity have been correlated with poor oncologic outcomes and a lack of response to anti-angiogenic therapies. Limited data exists on the impact of subcutaneous fat density (SFD) and visceral fat density (VFD) on onco
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::40d617dd61047cc9de4c4a26a0d81eea
https://doi.org/10.1016/j.ygyno.2021.02.032
https://doi.org/10.1016/j.ygyno.2021.02.032
Autor:
Gopichandh Danala, Seyedehnafiseh Mirniaharikandehei, Meredith Jones, Tiancheng Gai, Sai Kiran R. Maryada, Dee Wu, Yuchen Qiu, Bin Zheng
Publikováno v:
Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c8120210e09a9f27b7170289e583a4da
https://doi.org/10.1117/12.2607273
https://doi.org/10.1117/12.2607273
Assessment of a new CAD-generated imaging marker to predict risk of having mammography-occult tumors
Autor:
Seyedehnafiseh Mirniaharikandehei, Alan Hollingsworth, Meredith Jones, Hong Liu, Yuchen Qiu, Bin Zheng
Publikováno v:
Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0d83424977f30cd372509f983e8e8446
https://doi.org/10.1117/12.2607384
https://doi.org/10.1117/12.2607384
Autor:
Alan B. Hollingsworth, Morteza Heidari, Seyedehnafiseh Mirniaharikandehei, Wei Liu, Bin Zheng, Hong Liu
Publikováno v:
IEEE Trans Med Imaging
This study aims to develop and evaluate a new computer-aided diagnosis (CADx) scheme based on analysis of global mammographic image features to predict likelihood of cases being malignant. An image dataset involving 1,959 cases was retrospectively as
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::79417a7d8c050a1b55e774a567757404
https://doi.org/10.1109/tmi.2019.2946490
https://doi.org/10.1109/tmi.2019.2946490
Autor:
Sivaramakrishnan Lakshmivarahan, Seyedehnafiseh Mirniaharikandehei, Gopichandh Danala, Bin Zheng, Morteza Heidari
Publikováno v:
Medical Imaging 2021: Computer-Aided Diagnosis.
Developing radiomic based machine learning models has drawn considerable attention in recent years. However, identifying a small and optimal feature vector to build a robust machine learning models has always been a controversial issue. In this study
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::57f6194df44bcc1056621c553572601d
https://doi.org/10.1117/12.2580732
https://doi.org/10.1117/12.2580732
Autor:
Hung N. Pham, Morteza Heidari, Sivaramakrishnan Lakshmivarahan, Seyedehnafiseh Mirniaharikandehei, Abolfazl Zargari Khuzani, Gopichandh Danala, Bin Zheng
Publikováno v:
Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications.
The purpose of this study is to develop a machine learning model with the optimal features computed from mammograms to classify suspicious regions as benign and malignant. To this aim, we investigate the benefits of implementing a machine learning ap
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::528f1df383de652212c0424e8e49c21c
https://doi.org/10.1117/12.2580944
https://doi.org/10.1117/12.2580944
Autor:
Morteza Heidari, Seyedehnafiseh Mirniaharikandehei, Abolfazl Zargari Khuzani, Yuchen Qiu, Bin Zheng, Gopichandh Danala
Publikováno v:
Medical Imaging 2021: Computer-Aided Diagnosis.
As the rapid spread of coronavirus disease (COVID-19) worldwide, X-ray chest radiography has also been used to detect COVID-19 infected pneumonia and assess its severity or monitor its prognosis in the hospitals due to its low cost, low radiation dos
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e34375958d4407445612b898b6c6fcb5
https://doi.org/10.1117/12.2580948
https://doi.org/10.1117/12.2580948