Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework
Abstract Precision medicine approaches rely on obtaining precise knowledge of the true state of health of an individual patient, which results from a combination of their genetic risks and environmental exposures. This approach is currently limited by the lack of effective and efficient non-invasive...
Enregistré dans:
Auteurs principaux: | Luke Oakden-Rayner, Gustavo Carneiro, Taryn Bessen, Jacinto C. Nascimento, Andrew P. Bradley, Lyle J. Palmer |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2017
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/2f9f82700e384fee93b5e2cfdf3b8c55 |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
A survey of clinicians on the use of artificial intelligence in ophthalmology, dermatology, radiology and radiation oncology
par: Jane Scheetz, et autres
Publié: (2021) -
Observing deep radiomics for the classification of glioma grades
par: Kazuma Kobayashi, et autres
Publié: (2021) -
A CT-Based Radiomics Nomogram Integrated With Clinic-Radiological Features for Preoperatively Predicting WHO/ISUP Grade of Clear Cell Renal Cell Carcinoma
par: Yingjie Xv, et autres
Publié: (2021) -
Homocysteine and familial longevity: the Leiden Longevity Study.
par: Carolien A Wijsman, et autres
Publié: (2011) -
Intensity harmonization techniques influence radiomics features and radiomics-based predictions in sarcoma patients
par: Amandine Crombé, et autres
Publié: (2020)