Cargando…
Possible Bias in Supervised Deep Learning Algorithms for CT Lung Nodule Detection and Classification
SIMPLE SUMMARY: Artificial Intelligence (AI) algorithms can assist clinicians in their daily tasks by automatically detecting and/or classifying nodules in chest CT scans. Bias of such algorithms is one of the reasons why implementation of them in clinical practice is still not widely adopted. There...
Autores principales: | Sourlos, Nikos, Wang, Jingxuan, Nagaraj, Yeshaswini, van Ooijen, Peter, Vliegenthart, Rozemarijn |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9405732/ https://www.ncbi.nlm.nih.gov/pubmed/36010861 http://dx.doi.org/10.3390/cancers14163867 |
Ejemplares similares
-
Preparing CT imaging datasets for deep learning in lung nodule analysis: Insights from four well-known datasets
por: Wang, Jingxuan, et al.
Publicado: (2023) -
AI-Driven Model for Automatic Emphysema Detection in Low-Dose Computed Tomography Using Disease-Specific Augmentation
por: Nagaraj, Yeshaswini, et al.
Publicado: (2022) -
Evaluation of a novel deep learning–based classifier for perifissural nodules
por: Han, Daiwei, et al.
Publicado: (2020) -
Performance of computer-aided detection of pulmonary nodules in low-dose CT: comparison with double reading by nodule volume
por: Zhao, Yingru, et al.
Publicado: (2012) -
A practical approach to radiological evaluation of CT lung cancer screening examinations
por: Xie, Xueqian, et al.
Publicado: (2013)