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Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer2 years ago
Dual-energy CT–based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer2 years ago
Radiogenomics of lower-grade gliomas: machine learning–based MRI texture analysis for predicting 1p/19q codeletion status2 years ago
Combining DWI radiomics features with transurethral resection promotes the differentiation between muscle-invasive bladder cancer and non-muscle-invasive bladder cancer3 years ago
A machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma from preoperative diffusion-weighted imaging3 years ago
MRI-based radiomics nomogram may predict the response to induction chemotherapy and survival in locally advanced nasopharyngeal carcinoma3 years ago
Radiomic feature reproducibility in contrast-enhanced CT of the pancreas is affected by variabilities in scan parameters and manual segmentation3 years ago
Welcome to the blog on Artificial Intelligence of the European Society of Radiology
This blog aims at bringing educational and critical perspectives on AI to readers. It should help imaging professionals to learn and keep up to date with the technologies being developed in this rapidly evolving field.
Most used hashtags
- What do stakeholders think about the future of AI in radiology?
- Single-center versus multi-center biparametric MRI radiomics approach
- Do plaque-related factors affect the diagnostic performance of an AI-CADS?
- ESTI 2022 to feature multiple AI lectures (June 9-11, 2022)
- Precision of MRI radiomics features in the liver and HCC