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Deep learning–based scan range optimization can reduce radiation exposure in coronary CT angiography.
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- European Radiology, 2024, v. 34, n. 1, p. 411, doi. 10.1007/s00330-023-09971-9
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- Article
Pediatric age estimation from radiographs of the knee using deep learning.
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- European Radiology, 2022, v. 32, n. 7, p. 4813, doi. 10.1007/s00330-022-08582-0
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- Article
Ossification area localization in pediatric hand radiographs using deep neural networks for object detection.
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- PLoS ONE, 2018, v. 13, n. 11, p. 1, doi. 10.1371/journal.pone.0207496
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- Article
Evaluation of the dependence of radiomic features on the machine learning model.
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- Insights into Imaging, 2022, v. 13, n. 1, p. 1, doi. 10.1186/s13244-022-01170-2
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- Article
Measuring the bias of incorrect application of feature selection when using cross-validation in radiomics.
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- Insights into Imaging, 2021, v. 12, n. 1, p. 1, doi. 10.1186/s13244-021-01115-1
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- Article
frozen elephant trunk technique: impact of proximalization and the four-sites perfusion technique.
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- European Journal of Cardio-Thoracic Surgery, 2022, v. 61, n. 1, p. 195, doi. 10.1093/ejcts/ezab295
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- Article
Real-Life Experience in the Treatment of Intrahepatic Cholangiocarcinoma by <sup>90</sup>Y Radioembolization: A Multicenter Retrospective Study.
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- Journal of Nuclear Medicine, 2023, v. 64, n. 4, p. 529, doi. 10.2967/jnumed.122.264598
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- Article
Evaluation of 18F-FDG PET and DWI datasets for the prediction of therapy response of soft tissues sarcomas under neoadjuvant isolated limb perfusion.
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- Journal of Nuclear Medicine, 2020, v. 61, n. 12, p. 1, doi. 10.2967/jnumed.120.248260
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- Article
Evaluation of 18F-FDG PET and DWI datasets for the prediction of therapy response of soft tissues sarcomas under neoadjuvant isolated limb perfusion.
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- Journal of Nuclear Medicine, 2020, v. 61, n. 11, p. 1, doi. 10.2967/jnumed.120.248260
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- Article
Evaluation of 18F-FDG PET and DWI datasets for the prediction of therapy response of soft tissues sarcomas under neoadjuvant isolated limb perfusion.
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- Journal of Nuclear Medicine, 2020, v. 61, n. 10, p. 1, doi. 10.2967/jnumed.120.248260
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- Publication type:
- Article
Evaluation of 18F-FDG PET and DWI datasets for the prediction of therapy response of soft tissues sarcomas under neoadjuvant isolated limb perfusion.
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- Journal of Nuclear Medicine, 2020, v. 61, n. 9, p. 1, doi. 10.2967/jnumed.120.248260
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- Article
PET/MRI versus PET/CT in whole-body staging: results from a unicenter observational study in 1003 subsequent examinations.
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- Journal of Nuclear Medicine, 2019, v. 60, n. 12, p. 1, doi. 10.2967/jnumed.119.233940
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- Article
18F-FDG PET/MRI for therapy response assessment of isolated limb perfusion in patients with soft-tissue sarcomas.
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- Journal of Nuclear Medicine, 2019, v. 60, n. 12, p. 1, doi. 10.2967/jnumed.119.226761
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- Article
<sup>18</sup>F-FDG PET/MRI for Therapy Response Assessment of Isolated Limb Perfusion in Patients with Soft-Tissue Sarcomas.
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- Journal of Nuclear Medicine, 2019, v. 60, n. 11, p. 1537, doi. 10.2967/jnumed.119.226761
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- Article
Artificial Intelligence in Nuclear Medicine.
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- Journal of Nuclear Medicine, 2019, v. 60, p. 29S, doi. 10.2967/jnumed.118.220590
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- Article
Assessment of Ileocolonic Inflammation in Crohn's Disease: Which Surrogate Marker Is Better--MaRIA, Clermont, or PET/MR Index? Initial Results of a Feasibility Trial.
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- Journal of Nuclear Medicine, 2019, v. 60, n. 6, p. 851, doi. 10.2967/jnumed.118.216937
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- Article
Impact of AI-Based Post-Processing on Image Quality of Non-Contrast Computed Tomography of the Chest and Abdomen.
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- Diagnostics (2075-4418), 2024, v. 14, n. 6, p. 612, doi. 10.3390/diagnostics14060612
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- Article
Deep Features from Pretrained Networks Do Not Outperform Hand-Crafted Features in Radiomics.
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- Diagnostics (2075-4418), 2023, v. 13, n. 20, p. 3266, doi. 10.3390/diagnostics13203266
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- Article
Generating Virtual Short Tau Inversion Recovery (STIR) Images from T1- and T2-Weighted Images Using a Conditional Generative Adversarial Network in Spine Imaging.
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- Diagnostics (2075-4418), 2021, v. 11, n. 9, p. 1542, doi. 10.3390/diagnostics11091542
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- Article
Are deep models in radiomics performing better than generic models? A systematic review.
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- European Radiology Experimental, 2023, v. 7, n. 1, p. 1, doi. 10.1186/s41747-023-00325-0
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- Article
The effect of preprocessing filters on predictive performance in radiomics.
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- European Radiology Experimental, 2022, v. 6, n. 1, p. 1, doi. 10.1186/s41747-022-00294-w
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- Article
A rapid volume of interest-based approach of radiomics analysis of breast MRI for tumor decoding and phenotyping of breast cancer.
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- PLoS ONE, 2020, v. 15, n. 6, p. 1, doi. 10.1371/journal.pone.0234871
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- Article
The Fluid Dynamical Performance of the Carpentier-Edwards PERIMOUNT Magna Ease Prosthesis.
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- BioMed Research International, 2018, v. 2018, p. 1, doi. 10.1155/2018/5429594
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- Article
Detecting the pulmonary trunk in CT scout views using deep learning.
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- Scientific Reports, 2021, v. 11, n. 1, p. 1, doi. 10.1038/s41598-021-89647-w
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- Article
METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII.
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- Insights into Imaging, 2024, v. 15, n. 1, p. 1, doi. 10.1186/s13244-023-01572-w
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- Article
The effect of feature normalization methods in radiomics.
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- Insights into Imaging, 2024, v. 15, n. 1, p. 1, doi. 10.1186/s13244-023-01575-7
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- Article
Non-invasive tumor decoding and phenotyping of cerebral gliomas utilizing multiparametric 18F-FET PET-MRI and MR Fingerprinting.
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- European Journal of Nuclear Medicine & Molecular Imaging, 2020, v. 47, n. 6, p. 1435, doi. 10.1007/s00259-019-04602-2
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- Article
18F-FDG PET-MR enterography in predicting histological active disease using the Nancy index in ulcerative colitis: a randomized controlled trial.
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- European Journal of Nuclear Medicine & Molecular Imaging, 2020, v. 47, n. 4, p. 768, doi. 10.1007/s00259-019-04535-w
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- Article
Pediatric age estimation from thoracic and abdominal CT scout views using deep learning.
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- Scientific Reports, 2023, v. 13, n. 1, p. 1, doi. 10.1038/s41598-023-29296-3
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- Article
Prediction of left lobe hypertrophy after right lobe radioembolization of the liver using a clinical data model with external validation.
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- Scientific Reports, 2022, v. 12, n. 1, p. 1, doi. 10.1038/s41598-022-25077-6
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- Article
Evaluation of the Predictive Potential of 18F-FDG PET and DWI Data Sets for Relevant Prognostic Parameters of Primary Soft-Tissue Sarcomas.
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- Cancers, 2021, v. 13, n. 11, p. 2753, doi. 10.3390/cancers13112753
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- Article
Predictors of Symptomatic Intracranial Hemorrhage after Endovascular Thrombectomy in Acute Ischemic Stroke Patients with Anterior Large Vessel Occlusion—Procedure Time and Reperfusion Quality Determine.
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- Journal of Clinical Medicine, 2022, v. 11, n. 24, p. 7433, doi. 10.3390/jcm11247433
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18F-FDG PET/MR versus MR Alone in Whole-Body Primary Staging and Restaging of Patients with Rectal Cancer: What Is the Benefit of PET?
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- Journal of Clinical Medicine, 2020, v. 9, n. 10, p. 3163, doi. 10.3390/jcm9103163
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- Article
Diagnostic Performance of Simultaneous [18F]-FDG PET/MR for Assessing Endoscopically Active Inflammation in Patients with Ulcerative Colitis: A Prospective Study.
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- Journal of Clinical Medicine, 2020, v. 9, n. 8, p. 2474, doi. 10.3390/jcm9082474
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- Article
Determining the anatomical site in knee radiographs using deep learning.
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- Scientific Reports, 2022, v. 12, n. 1, p. 1, doi. 10.1038/s41598-022-08020-7
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- Article
Applying oversampling before cross-validation will lead to high bias in radiomics.
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- Scientific Reports, 2024, v. 11, n. 1, p. 1, doi. 10.1038/s41598-024-62585-z
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- Article
The effect of data resampling methods in radiomics.
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- Scientific Reports, 2024, v. 14, n. 1, p. 1, doi. 10.1038/s41598-024-53491-5
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- Article
Determining body height and weight from thoracic and abdominal CT localizers in pediatric and young adult patients using deep learning.
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- Scientific Reports, 2023, v. 13, n. 1, p. 1, doi. 10.1038/s41598-023-46080-5
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- Article