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Radiomics
Radiomics is a field of medical study that aims to extract large amount of quantitative features from medical images using data-characterisation…
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Related topics
Related topics
5 relations
CT scan
Co-occurrence matrix
Computational anatomy
Image texture
Broader (1)
Medical imaging
Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
Review
2020
Review
2020
Regulatory issues for artificial intelligence in radiology
F. Pesapane
,
M. Suter
,
F. Sardanelli
2020
Corpus ID: 213658522
2019
2019
Preoperative Prediction of Infection Stones Using Radiomics Features From Computed Tomography
Xiaoyu Cui
,
Fengying Che
,
+6 authors
Gejun Zhang
IEEE Access
2019
Corpus ID: 202561049
Preoperative prediction of infection stones from CT images could provide additional information for treatment planning. We…
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2019
2019
Radiomics to Predict Prostate CancerAggressiveness: A Preliminary Study
D. Germanese
,
E. Bertelli
,
+8 authors
A. Barucci
International Conferences on Biological…
2019
Corpus ID: 209495668
Radiomics is encouraging a paradigm shift in oncological diagnostics towards the symbiosis of radiology and Artificial…
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2019
2019
Evaluation of Feature Robustness Against Technical Parameters in CT Radiomics: Verification of Phantom Study with Patient Dataset
Hyeong-min Jin
,
Jong Hyo Kim
Journal of Signal Processing Systems
2019
Corpus ID: 212637783
Recent advances in radiomics have shown promising results in prognostic and diagnostic studies with high dimensional imaging…
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2018
2018
Radiomics to Predict Response to Neoadjuvant Chemotherapy in Rectal Cancer: Influence of Simultaneous Feature Selection and Classifier Optimization
S. Rosati
,
C. M. Gianfreda
,
G. Balestra
,
V. Giannini
,
S. Mazzetti
,
D. Regge
IEEE Life Sciences Conference (LSC)
2018
Corpus ID: 56177906
According to the guidelines, patients with locally advanced colorectal cancer undergo neoadjuvant chemotherapy. However, response…
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2017
2017
Perturb-and-MPM: Quantifying Segmentation Uncertainty in Dense Multi-Label CRFs
Raphael Meier
,
Urspeter Knecht
,
Alain Jungo
,
R. Wiest
,
M. Reyes
arXiv.org
2017
Corpus ID: 5841105
This paper proposes a novel approach for uncertainty quantification in dense Conditional Random Fields (CRFs). The presented…
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2017
2017
Restricted Connectivity in Deep Neural Networks
Yani A Ioannou
2017
Corpus ID: 28163175
Convolutional networks are at the core of most stateof-the-art computer vision solutions for a wide variety of tasks. Since 2014…
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2015
2015
Prostate Cancer Detection via a Quantitative Radiomics-Driven Conditional Random Field Framework
A. Chung
,
F. Khalvati
,
M. Shafiee
,
M. Haider
,
A. Wong
IEEE Access
2015
Corpus ID: 6430568
The use of high-volume quantitative radiomics features extracted from multi-parametric magnetic resonance imaging (MP-MRI) is…
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Review
2015
Review
2015
Discovery Radiomics for Multi-Parametric MRI Prostate Cancer Detection
A. Chung
,
M. Shafiee
,
Devinder Kumar
,
F. Khalvati
,
M. Haider
,
A. Wong
arXiv.org
2015
Corpus ID: 9601680
Prostate cancer is the most diagnosed form of cancer in Canadian men, and is the third leading cause of cancer death. Despite…
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2014
2014
HPV is a truly independent risk factor and predictor of survival in oropharyngeal cancer, and is a necessary adjunct to the UICC staging system in this disease.
Michael F. Moran
,
L. Anderson
,
J. James
,
D. McCance
2014
Corpus ID: 53385512
Background: Human papilloma virus positive (HPV(+)) oropharyngeal squamous cell carcinoma (OPSCC) has a markedly improved…
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