Cargando…
Integration of Radiomics and Tumor Biomarkers in Interpretable Machine Learning Models
SIMPLE SUMMARY: Artificial intelligence (AI) based on deep neural networks (DNNs) has demonstrated great performance in computer vision. However, their clinical application in the diagnosis and prognosis of cancer using medical imaging has been limited. Not knowing the AI’s decision-making process (...
Autores principales: | Brocki, Lennart, Chung, Neo Christopher |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10177141/ https://www.ncbi.nlm.nih.gov/pubmed/37173930 http://dx.doi.org/10.3390/cancers15092459 |
Ejemplares similares
-
Machine Learning methods for Quantitative Radiomic Biomarkers
por: Parmar, Chintan, et al.
Publicado: (2015) -
MACHINE LEARNING AND RADIOMICS IDENTIFY NOVEL BIOMARKERS OF BONE LOSS
por: Haudenschild, A.K., et al.
Publicado: (2022) -
Machine Learning and Integrative Analysis of Biomedical Big Data
por: Mirza, Bilal, et al.
Publicado: (2019) -
Machine Learning-Based Radiomics Predicting Tumor Grades and Expression of Multiple Pathologic Biomarkers in Gliomas
por: Gao, Min, et al.
Publicado: (2020) -
Applications of radiomics and machine learning for radiotherapy of malignant brain tumors
por: Kocher, Martin, et al.
Publicado: (2020)