Cargando…
MS‐Net: Learning to assess the malignant status of a lung nodule by a radiologist and her peers
BACKGROUND: Automatically assessing the malignant status of lung nodules based on CTscan images can help reduce the workload of radiologists while improving their diagnostic accuracy. PURPOSE: Despite remarkable progress in the automatic diagnosis of pulmonary nodules by deep learning technologies,...
Autores principales: | Dai, Duwei, Dong, Caixia, Li, Zongfang, Xu, Songhua |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10338807/ https://www.ncbi.nlm.nih.gov/pubmed/36929569 http://dx.doi.org/10.1002/acm2.13964 |
Ejemplares similares
-
The Evolution and Disparities of Online Attitudes Toward COVID-19 Vaccines: Year-long Longitudinal and Cross-sectional Study
por: Zhang, Chunyan, et al.
Publicado: (2022) -
Transient Osteoporosis of the Hip: A Radiologist’s Perspective
por: Khan, Muhammad Mehraiz, et al.
Publicado: (2022) -
Does a Deep Learning–Based Computer-Assisted Diagnosis System Outperform Conventional Double Reading by Radiologists in Distinguishing Benign and Malignant Lung Nodules?
por: Liu, Zhou, et al.
Publicado: (2020) -
Survey of brachytherapy training experience among radiation oncology trainees and fellows in the Royal Australian and New Zealand College of Radiologists (RANZCR)
por: Ong, Wee Loon, et al.
Publicado: (2022) -
The Efficacy of Hemostatic Radiotherapy for Advanced Malignancies Assessed by World Health Organization Bleeding Status
por: Katano, Atsuto, et al.
Publicado: (2021)