Applications and limitations of radiomics
SSF Yip, HJWL Aerts - Physics in Medicine & Biology, 2016 - iopscience.iop.org
Radiomics is an emerging field in quantitative imaging that uses advanced imaging features
to objectively and quantitatively describe tumour phenotypes. Radiomic features have …
to objectively and quantitatively describe tumour phenotypes. Radiomic features have …
Radiomics: a new application from established techniques
The increasing use of biomarkers in cancer have led to the concept of personalized
medicine for patients. Personalized medicine provides better diagnosis and treatment …
medicine for patients. Personalized medicine provides better diagnosis and treatment …
Diagnosis of benign and malignant breast lesions on DCE‐MRI by using radiomics and deep learning with consideration of peritumor tissue
J Zhou, Y Zhang, KT Chang, KE Lee… - Journal of Magnetic …, 2020 - Wiley Online Library
Background Computer‐aided methods have been widely applied to diagnose lesions
detected on breast MRI, but fully‐automatic diagnosis using deep learning is rarely reported …
detected on breast MRI, but fully‐automatic diagnosis using deep learning is rarely reported …
Deep learning based liver cancer detection using watershed transform and Gaussian mixture model techniques
Objectives Liver cancer is one of the leading cause of death in all over the world. Detecting
the cancer tissue manually is a difficult task and time consuming. Hence, a computer-aided …
the cancer tissue manually is a difficult task and time consuming. Hence, a computer-aided …
Radiomics in breast MRI: Current progress toward clinical application in the era of artificial intelligence
H Satake, S Ishigaki, R Ito, S Naganawa - La radiologia medica, 2022 - Springer
Breast magnetic resonance imaging (MRI) is the most sensitive imaging modality for breast
cancer diagnosis and is widely used clinically. Dynamic contrast-enhanced MRI is the basis …
cancer diagnosis and is widely used clinically. Dynamic contrast-enhanced MRI is the basis …
Breast cancer diagnosis in DCE-MRI using mixture ensemble of convolutional neural networks
This work addresses a novel computer-aided diagnosis (CAD) system in breast dynamic
contrast-enhanced magnetic resonance imaging (DCE-MRI). The CAD system is designed …
contrast-enhanced magnetic resonance imaging (DCE-MRI). The CAD system is designed …
Gray-level invariant Haralick texture features
Haralick texture features are common texture descriptors in image analysis. To compute the
Haralick features, the image gray-levels are reduced, a process called quantization. The …
Haralick features, the image gray-levels are reduced, a process called quantization. The …
Machine learning in breast MRI
Machine‐learning techniques have led to remarkable advances in data extraction and
analysis of medical imaging. Applications of machine learning to breast MRI continue to …
analysis of medical imaging. Applications of machine learning to breast MRI continue to …
Data analysis strategies in medical imaging
Radiographic imaging continues to be one of the most effective and clinically useful tools
within oncology. Sophistication of artificial intelligence has allowed for detailed …
within oncology. Sophistication of artificial intelligence has allowed for detailed …
The ACR BI-RADS® experience: learning from history
ES Burnside, EA Sickles, LW Bassett, DL Rubin… - Journal of the American …, 2009 - Elsevier
The Breast Imaging Reporting and Data System®(BI-RADS®) initiative, instituted by the
ACR, was begun in the late 1980s to address a lack of standardization and uniformity in …
ACR, was begun in the late 1980s to address a lack of standardization and uniformity in …