1. Radiomics and Deep Learning: Hepatic Applications
- Author
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Seung Soo Lee, Bumwoo Park, and Hyo Jung Park
- Subjects
Liver Cirrhosis ,Artificial intelligence ,medicine.medical_specialty ,Databases, Factual ,Review Article ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,Liver disease ,0302 clinical medicine ,Radiomics ,Liver Fibroses ,Hypertension, Portal ,Image Processing, Computer-Assisted ,medicine ,Humans ,Computer-assisted ,Radiology, Nuclear Medicine and imaging ,business.industry ,Liver Diseases ,Deep learning ,Liver Neoplasms ,medicine.disease ,Liver ,ROC Curve ,Area Under Curve ,030220 oncology & carcinogenesis ,Gastrointestinal Imaging ,Portal hypertension ,Radiology ,business - Abstract
Radiomics and deep learning have recently gained attention in the imaging assessment of various liver diseases. Recent research has demonstrated the potential utility of radiomics and deep learning in staging liver fibroses, detecting portal hypertension, characterizing focal hepatic lesions, prognosticating malignant hepatic tumors, and segmenting the liver and liver tumors. In this review, we outline the basic technical aspects of radiomics and deep learning and summarize recent investigations of the application of these techniques in liver disease.
- Published
- 2020