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- 2022
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Mark
Freely available convolutional neural network-based quantification of PET/CT lesions is associated with survival in patients with lung cancer
(
- Contribution to journal › Article
- 2021
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Mark
Artificial intelligence-aided CT segmentation for body composition analysis : a validation study
(
- Contribution to journal › Article
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Mark
AI-based detection of lung lesions in [18F]FDG PET-CT from lung cancer patients
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- 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
- 2020
-
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
- 2018
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Mark
Evaluation of changes in Bone Scan Index at different acquisition time-points in bone scintigraphy
(
- Contribution to journal › Article
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Mark
A prospective study to evaluate the intra-individual reproducibility of bone scans for quantitative assessment in patients with metastatic prostate cancer
(
- Contribution to journal › Article
- 2017
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Mark
Automated evaluation of normal uptake in different skeletal parts in 18F-sodium fluoride (NaF) PET/CT using a new convolutional neural network method
(
- Contribution to conference › Abstract