Cargando…
A Pipeline for the Implementation and Visualization of Explainable Machine Learning for Medical Imaging Using Radiomics Features
Machine learning (ML) models have been shown to predict the presence of clinical factors from medical imaging with remarkable accuracy. However, these complex models can be difficult to interpret and are often criticized as “black boxes”. Prediction models that provide no insight into how their pred...
Autores principales: | Severn, Cameron, Suresh, Krithika, Görg, Carsten, Choi, Yoon Seong, Jain, Rajan, Ghosh, Debashis |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9318445/ https://www.ncbi.nlm.nih.gov/pubmed/35890885 http://dx.doi.org/10.3390/s22145205 |
Ejemplares similares
-
Survival prediction models: an introduction to discrete-time modeling
por: Suresh, Krithika, et al.
Publicado: (2022) -
Causal Inference in Radiomics: Framework, Mechanisms, and Algorithms
por: Ghosh, Debashis, et al.
Publicado: (2022) -
An Explainable Machine Learning Pipeline for Stroke Prediction on Imbalanced Data
por: Kokkotis, Christos, et al.
Publicado: (2022) -
Uncertainty-Aware Deep Learning Classification of Adamantinomatous Craniopharyngioma from Preoperative MRI
por: Prince, Eric W., et al.
Publicado: (2023) -
Are radiomic signatures ready for incorporation in the clinical pipeline?
por: Singh, Apurva, et al.
Publicado: (2023)