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Automated graded prognostic assessment for patients with hepatocellular carcinoma using machine learning.
- Published in:
- European Radiology, 2024, v. 34, n. 10, p. 6940, doi. 10.1007/s00330-024-10624-8
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- Article
Automated MRI liver segmentation for anatomical segmentation, liver volumetry, and the extraction of radiomics.
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- European Radiology, 2024, v. 34, n. 8, p. 5056, doi. 10.1007/s00330-023-10495-5
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- Article
Improved performance and consistency of deep learning 3D liver segmentation with heterogeneous cancer stages in magnetic resonance imaging.
- Published in:
- PLoS ONE, 2021, v. 16, n. 12, p. 1, doi. 10.1371/journal.pone.0260630
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- Article
Impact of <sup>18</sup>F-FDG PET Intensity Normalization on Radiomic Features of Oropharyngeal Squamous Cell Carcinomas and Machine Learning-Generated Biomarkers.
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- Journal of Nuclear Medicine, 2024, v. 65, n. 8, p. 1, doi. 10.2967/jnumed.123.266637
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- 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
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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- 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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- Article