Found: 19
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Machine Learning Models for Classifying High- and Low-Grade Gliomas: A Systematic Review and Quality of Reporting Analysis.
- Published in:
- Frontiers in Oncology, 2022, v. 12, p. 1, doi. 10.3389/fonc.2022.856231
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- Publication type:
- Article
Machine Learning Tools for Image-Based Glioma Grading and the Quality of Their Reporting: Challenges and Opportunities.
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- Cancers, 2022, v. 14, n. 11, p. 2623, doi. 10.3390/cancers14112623
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- Publication type:
- Article
Machine Learning Applications for Differentiation of Glioma from Brain Metastasis—A Systematic Review.
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- Cancers, 2022, v. 14, n. 6, p. 1369, doi. 10.3390/cancers14061369
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- Publication type:
- Article
Potential Added Value of PET/CT Radiomics for Survival Prognostication beyond AJCC 8th Edition Staging in Oropharyngeal Squamous Cell Carcinoma.
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- Cancers, 2020, v. 12, n. 7, p. 1778, doi. 10.3390/cancers12071778
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- Article
Peri-hematomal edema shape features related to 3-month outcome in acute supratentorial intracerebral hemorrhage.
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- European Stroke Journal, 2024, v. 9, n. 2, p. 383, doi. 10.1177/23969873231223814
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- Article
Reliable prediction of survival in advanced-stage hepatocellular carcinoma treated with sorafenib: comparing 1D and 3D quantitative tumor response criteria on MRI.
- Published in:
- 2021
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- Publication type:
- journal article
Radiomic markers of intracerebral hemorrhage expansion on non-contrast CT: independent validation and comparison with visual markers.
- Published in:
- Frontiers in Neuroscience, 2023, p. 1, doi. 10.3389/fnins.2023.1225342
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- Publication type:
- Article
PET/CT radiomics signature of human papilloma virus association in oropharyngeal squamous cell carcinoma.
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- European Journal of Nuclear Medicine & Molecular Imaging, 2020, v. 47, n. 13, p. 2978, doi. 10.1007/s00259-020-04839-2
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- Publication type:
- Article
Admission computed tomography radiomic signatures outperform hematoma volume in predicting baseline clinical severity and functional outcome in the ATACH‐2 trial intracerebral hemorrhage population.
- Published in:
- European Journal of Neurology, 2021, v. 28, n. 9, p. 2989, doi. 10.1111/ene.15000
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- Publication type:
- Article
Predicting tumor recurrence on baseline MR imaging in patients with early-stage hepatocellular carcinoma using deep machine learning.
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- Scientific Reports, 2023, v. 13, n. 1, p. 1, doi. 10.1038/s41598-023-34439-7
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- Publication type:
- Article
Clinical implementation of artificial intelligence in neuroradiology with development of a novel workflow-efficient picture archiving and communication system-based automated brain tumor segmentation and radiomic feature extraction.
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- Frontiers in Neuroscience, 2022, v. 16, p. 1, doi. 10.3389/fnins.2022.860208
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- Publication type:
- Article
Uncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan.
- Published in:
- NPJ Digital Medicine, 2024, v. 7, p. 1, doi. 10.1038/s41746-024-01007-w
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- Publication type:
- Article
Uncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan.
- Published in:
- NPJ Digital Medicine, 2024, v. 7, n. 1, p. 1, doi. 10.1038/s41746-024-01007-w
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- Publication type:
- Article
Radiomics-Based Prediction of Collateral Status from CT Angiography of Patients Following a Large Vessel Occlusion Stroke.
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- Diagnostics (2075-4418), 2024, v. 14, n. 5, p. 485, doi. 10.3390/diagnostics14050485
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- Publication type:
- Article
Effects of ambient conditions on the risk of pressure injuries in bedridden patients—multi‐physics modelling of microclimate.
- Published in:
- International Wound Journal, 2018, v. 15, n. 3, p. 402, doi. 10.1111/iwj.12877
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- Publication type:
- Article
Deep learning for prediction of post-thrombectomy outcomes based on admission CT angiography in large vessel occlusion stroke.
- Published in:
- Frontiers in Artificial Intelligence, 2024, p. 01, doi. 10.3389/frai.2024.1369702
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- Publication type:
- Article
Identifying clinically applicable machine learning algorithms for glioma segmentation: recent advances and discoveries.
- Published in:
- Neuro-Oncology Advances, 2022, v. 4, n. 1, p. 1, doi. 10.1093/noajnl/vdac093
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- Publication type:
- Article
Impact of <sup>18</sup>F-FDG PET Intensity Normalization on Radiomic Features of Oropharyngeal Squamous Cell Carcinomas and Machine Learning-Generated Biomarkers.
- Published in:
- Journal of Nuclear Medicine, 2024, v. 65, n. 8, p. 1, doi. 10.2967/jnumed.123.266637
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- Publication type:
- Article
Impact of <sup>18</sup>F-FDG PET Intensity Normalization on Radiomic Features of Oropharyngeal Squamous Cell Carcinomas and Machine Learning-Generated Biomarkers.
- Published in:
- Journal of Nuclear Medicine, 2024, v. 65, n. 5, p. 803, doi. 10.2967/jnumed.123.266637
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- Publication type:
- Article