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Open AccessAutomated graded prognostic assessment for patients with hepatocellular carcinoma using machine learning
Accurate mortality risk quantification is crucial for the management of hepatocellular carcinoma (HCC); however, most scoring systems are subjective.
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Open AccessUncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan
Hematoma expansion (HE) is a modifiable risk factor and a potential treatment target in patients with intracerebral hemorrhage (ICH). We aimed to train and validate deep-learning models for high-confidence pre...
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Open AccessAutomated MRI liver segmentation for anatomical segmentation, liver volumetry, and the extraction of radiomics
To develop and evaluate a deep convolutional neural network (DCNN) for automated liver segmentation, volumetry, and radiomic feature extraction on contrast-enhanced portal venous phase magnetic resonance imagi...
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Open AccessKünstliche Intelligenz in der Hals-Nasen-Ohren-Heilkunde
Die fortschreitende Digitalisierung ermöglicht zunehmend den Einsatz von künstlicher Intelligenz (KI). Sie wird Gesellschaft und Medizin in den nächsten Jahren maßgeblich beeinflussen.
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PET/CT radiomics signature of human papilloma virus association in oropharyngeal squamous cell carcinoma
To devise, validate, and externally test PET/CT radiomics signatures for human papillomavirus (HPV) association in primary tumors and metastatic cervical lymph nodes of oropharyngeal squamous cell carcinoma (O...
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Open AccessApplications of radiomics in precision diagnosis, prognostication and treatment planning of head and neck squamous cell carcinomas
Recent advancements in computational power, machine learning, and artificial intelligence technology have enabled automated evaluation of medical images to generate quantitative diagnostic and prognostic bioma...
