Cargando…
An artificial intelligence-assisted diagnosis modeling software (AIMS) platform based on medical images and machine learning: a development and validation study
BACKGROUND: Supervised machine learning methods [both radiomics and convolutional neural network (CNN)-based deep learning] are usually employed to develop artificial intelligence models with medical images for computer-assisted diagnosis and prognosis of diseases. A classical machine learning-based...
Autores principales: | Zhou, Zhiyong, Qian, Xusheng, Hu, Jisu, Chen, Guangqiang, Zhang, Caiyuan, Zhu, Jianbing, Dai, Yakang |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10644131/ https://www.ncbi.nlm.nih.gov/pubmed/37969634 http://dx.doi.org/10.21037/qims-23-20 |
Ejemplares similares
-
Multi-phase-combined CECT radiomics models for Fuhrman grade prediction of clear cell renal cell carcinoma
por: Zhou, Zhiyong, et al.
Publicado: (2023) -
macJNet: weakly-supervised multimodal image deformable registration using joint learning framework and multi-sampling cascaded MIND
por: Zhou, Zhiyong, et al.
Publicado: (2023) -
Beginnings of Artificial Intelligence in Medicine (AIM): Computational Artifice Assisting Scientific Inquiry and Clinical Art – with Reflections on Present AIM Challenges
por: Kulikowski, Casimir A.
Publicado: (2019) -
Development and validation of an infrared-artificial intelligence software for breast cancer detection
por: Martín-Del-Campo-Mena, Enrique, et al.
Publicado: (2023) -
Automated quantitative assessment of pediatric blunt hepatic trauma by deep learning-based CT volumetry
por: Huang, Shungen, et al.
Publicado: (2022)