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- 2024
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Mark
Improving sensitivity through data augmentation with synthetic lymph node metastases for AI-based analysis of PSMA PET-CT images
2024) In Clinical Physiology and Functional Imaging(
- Contribution to journal › Article
- 2023
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Mark
Automated quantification of PET/CT skeletal tumor burden in prostate cancer using artificial intelligence : The PET index
(
- Contribution to journal › Article
- 2022
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Mark
Deep learning takes the pain out of back breaking work - Automatic vertebral segmentation and attenuation measurement for osteoporosis
(
- Contribution to journal › Article
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Mark
Freely available artificial intelligence for pelvic lymph node metastases in PSMA PET-CT that performs on par with nuclear medicine physicians
(
- Contribution to journal › Article
- 2021
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Mark
Artificial intelligence-based detection of lymph node metastases by PET/CT predicts prostate cancer-specific survival
(
- Contribution to journal › Article
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Mark
Artificial intelligence-based measurements of PET/CT imaging biomarkers are associated with disease-specific survival of high-risk prostate cancer patients
(
- Contribution to journal › Article
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Mark
Automated artificial intelligence-based analysis of skeletal muscle volume predicts overall survival after cystectomy for urinary bladder cancer
(
- Contribution to journal › Article
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Mark
Post-reconstruction enhancement of [18F]FDG PET images with a convolutional neural network
(
- Contribution to journal › Article
- 2020
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Mark
Denoising of Scintillation Camera Images Using a Deep Convolutional Neural Network : A Monte Carlo Simulation Approach
(
- Contribution to journal › Article
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Mark
RECOMIA—a cloud-based platform for artificial intelligence research in nuclear medicine and radiology
(
- Contribution to journal › Article
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Mark
Deep learning-based quantification of PET/CT prostate gland uptake : association with overall survival
(
- Contribution to journal › Article
- 2019
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Mark
Deep learning for segmentation of 49 selected bones in CT scans : First step in automated PET/CT-based 3D quantification of skeletal metastases
(
- Contribution to journal › Article
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Mark
Automated quantification of reference levels in liver and mediastinal blood pool for the Deauville therapy response classification using FDG-PET/CT in Hodgkin and non-Hodgkin lymphomas
(
- Contribution to journal › Article