Zobrazeno 1 - 10
of 97 891
pro vyhledávání: '"Chest radiographs"'
Autor:
Hasenstab, Kyle A.1,2 (AUTHOR) kylehasenstab@gmail.com, Hahn, Lewis2 (AUTHOR), Chao, Nick1 (AUTHOR), Hsiao, Albert2 (AUTHOR)
Publikováno v:
Scientific Reports. 10/18/2024, Vol. 14 Issue 1, p1-12. 12p.
Autor:
Kaster, Lennard, Klein, Henriette, Marka, Alexander W., Urban, Theresa, Karl, Sandra, Gassert, Florian T., Steinhelfer, Lisa, Makowski, Marcus R., Pfeiffer, Daniela, Pfeiffer, Franz
Objectives: Evaluating the effects and artifacts introduced by medical foreign bodies in clinical dark-field chest radiographs and assessing their influence on the evaluation of pulmonary tissue, compared to conventional radiographs. Material & Metho
Externí odkaz:
http://arxiv.org/abs/2408.10855
Autor:
Kim, Duk Ju1 (AUTHOR), Nam, In Chul1 (AUTHOR) sky_hall@naver.com, Kim, Doo Ri1 (AUTHOR), Kim, Jeong Jae1 (AUTHOR), Hwang, Im-kyung1 (AUTHOR), Lee, Jeong Sub1 (AUTHOR), Park, Sung Eun2 (AUTHOR), Kim, Hyeonwoo3 (AUTHOR)
Publikováno v:
PLoS ONE. 8/12/2024, Vol. 19 Issue 8, p1-10. 10p.
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract Explainability of convolutional neural networks (CNNs) is integral for their adoption into radiological practice. Commonly used attribution methods localize image areas important for CNN prediction but do not characterize relevant imaging fe
Externí odkaz:
https://doaj.org/article/933854f3e3f34caebec4b7f8fcd1d012
Autor:
Chandrashekar, Mayanka, Goethert, Ian, Haque, Md Inzamam Ul, McMahon, Benjamin, Dhaubhadel, Sayera, Knight, Kathryn, Erdos, Joseph, Reagan, Donna, Taylor, Caroline, Kuzmak, Peter, Gaziano, John Michael, McAllister, Eileen, Costa, Lauren, Ho, Yuk-Lam, Cho, Kelly, Tamang, Suzanne, Fodeh-Jarad, Samah, Ovchinnikova, Olga S., Justice, Amy C., Hinkle, Jacob, Danciu, Ioana
Objectives: This study aims to assess the impact of domain shift on chest X-ray classification accuracy and to analyze the influence of ground truth label quality and demographic factors such as age group, sex, and study year. Materials and Methods:
Externí odkaz:
http://arxiv.org/abs/2407.21149
Autor:
Zhou, Yiliang, Ong, Hanley, Kennedy, Patrick, Wu, Carol, Kazam, Jacob, Hentel, Keith, Flanders, Adam, Shih, George, Peng, Yifan
The study examines the application of GPT-4V, a multi-modal large language model equipped with visual recognition, in detecting radiological findings from a set of 100 chest radiographs and suggests that GPT-4V is currently not ready for real-world d
Externí odkaz:
http://arxiv.org/abs/2403.15528
Akademický článek
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Autor:
López Alcolea, Julia1 (AUTHOR) anuskafer83@hotmail.com, Fernández Alfonso, Ana1 (AUTHOR) rcanoalonso@gmail.com, Cano Alonso, Raquel1 (AUTHOR) anaalvarezvazquez@gmail.com, Álvarez Vázquez, Ana1 (AUTHOR) adiaz_14@hotmail.com, Díaz Moreno, Alejandro1 (AUTHOR) dvdgarcia1995@gmail.com, García Castellanos, David1 (AUTHOR) lucia.sanabria@quironsalud.es, Sanabria Greciano, Lucía1 (AUTHOR) chawarhayoun@gmail.com, Hayoun, Chawar1 (AUTHOR) mrmrecio@gmail.com, Recio Rodríguez, Manuel1 (AUTHOR) vmartinezdevega@malvaluz.com, Andreu Vázquez, Cristina2 (AUTHOR) cristina.andreu@universidadeuropea.es, Thuissard Vasallo, Israel John2 (AUTHOR) israeljohn.thuissard@universidadeuropea.es, Martínez de Vega, Vicente1 (AUTHOR)
Publikováno v:
Diagnostics (2075-4418). Nov2024, Vol. 14 Issue 22, p2592. 14p.
Autor:
Wienholt, Patrick, Hermans, Alexander, Khader, Firas, Puladi, Behrus, Leibe, Bastian, Kuhl, Christiane, Nebelung, Sven, Truhn, Daniel
This study investigates the application of ordinal regression methods for categorizing disease severity in chest radiographs. We propose a framework that divides the ordinal regression problem into three parts: a model, a target function, and a class
Externí odkaz:
http://arxiv.org/abs/2402.05685