RECOMIA—a cloud-based platform for artificial intelligence research in nuclear medicine and radiology
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Lars Edenbrandt | Elin Trägårdh | Pablo Borrelli | Reza Kaboteh | Tony Gillberg | Johannes Ulén | Olof Enqvist | L. Edenbrandt | Reza Kaboteh | E. Trägårdh | P. Borrelli | O. Enqvist | J. Ulén | Tony Gillberg
[1] F. Kahl,et al. 3D skeletal uptake of 18F sodium fluoride in PET/CT images is associated with overall survival in patients with prostate cancer , 2017, EJNMMI Research.
[2] May Sadik,et al. 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 , 2018, Clinical physiology and functional imaging.
[3] Robert Ford,et al. RECIST 1.1 - Standardisation and disease-specific adaptations: Perspectives from the RECIST Working Group. , 2016, European journal of cancer.
[4] Yuichiro Hayashi,et al. A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentation , 2018, MICCAI.
[5] Erich P Huang,et al. RECIST 1.1-Update and clarification: From the RECIST committee. , 2016, European journal of cancer.
[6] Olof Enqvist,et al. Artificial intelligence‐based versus manual assessment of prostate cancer in the prostate gland: a method comparison study , 2019, Clinical physiology and functional imaging.
[7] Yan Wang,et al. Abdominal multi-organ segmentation with organ-attention networks and statistical fusion , 2018, Medical Image Anal..
[8] H. Jang,et al. Comparison of the RECIST and PERCIST criteria in solid tumors: a pooled analysis and review , 2016, Oncotarget.
[9] Lars Edenbrandt,et al. Deep learning for segmentation of 49 selected bones in CT scans: First step in automated PET/CT-based 3D quantification of skeletal metastases. , 2019, European journal of radiology.
[10] L. Edenbrandt,et al. The use of a proposed updated EARL harmonization of 18F-FDG PET-CT in patients with lymphoma yields significant differences in Deauville score compared with current EARL recommendations , 2019, EJNMMI Research.
[11] Hilde van der Togt,et al. Publisher's Note , 2003, J. Netw. Comput. Appl..
[12] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[13] Olof Enqvist,et al. Deep learning‐based quantification of PET/CT prostate gland uptake: association with overall survival , 2019, Clinical physiology and functional imaging.