Get involved in our projects, or apply our tools to your data.
RECOMIA invites nuclear medicine specialists, radiologists, technologists, physicists and others interested in AI and medical imaging to participate in our projects.
All participants get one month free access to the e-learning site eScan Academy when the project is finished.
Are you part of a research group? Are you interested in AI in medical imaging? Do you want to apply our AI-based tools to your data?
Request access by filling out our online form, or send us a mail to firstname.lastname@example.org.
The following three oral presentations at the upcoming European Congress of Radiology 2020, March 11-15 in Vienna, Austria, are based on the RECOMIA platform:
RECOMIA – a Cloud-based platform for Artificial Intelligence research in radiology. Borrelli et al.
Automated artificial intelligence-based measurements of biomarkers in 18-F choline PET/CT are associated with disease-specific survival of high-risk prostate cancer patients. Polymeri et al.
Prostate cancer lymph node metastasis assessment by artificial intelligence-based PET/CT detection. Borrelli et al.
Two abstracts at the Radiological Society of North America (RSNA) 2019 Scientific Assembly and Annual Meeting, December 1 - December 6, 2019, Chicago were based on the RECOMIA platform:
Automatic Acquired 18F-Choline PET/CT Biomarkers Association with Prognostic Value in High-Risk Prostate Cancer Patients. Borrelli P et al.
Deep Learning Takes the Pain Out of Back Breaking Work - Automatic Vertebral Segmentation and Attenuation Measurement for Opportunistic Osteoporosis Screening. Schmidt D et al. Poster presentation
Deep learning‐based quantification of PET/CT prostate gland uptake: association with overall survival. Polymeri E, Sadik M, Kaboteh R, Borrelli P, Enqvist O, Ulén J, Ohlsson M, Trägårdh E, Poulsen MH, Simonsen JA, Hoilund-Carlsen PF, Johnsson ÅA, Edenbrandt L. Clin Physiol Funct Imaging. 2019; Dec 3. doi: 10.1111/cpf.12611[Epub ahead of print]
Artificial intelligence-based versus manual assessment of prostate cancer in the prostate gland: a method comparison study. Mortensen MA, Borrelli P, Poulsen MH, Gerke O, Enqvist O, Ulén J, Trägårdh E, Constantinescu C, Edenbrandt L, Lund L, Høilund-Carlsen PF. Clin Physiol Funct Imaging. 2019:39;399-406.
Deep learning for segmentation of 49 selected bones in CT scans: First step in automated PET/CT-based 3D quantification of skeletal metastases. Lindgren Belal S, Sadik M, Kaboteh R, Enqvist O, Ulén J, Poulsen MH, Simonsen J, Høilund-Carlsen PF, Edenbrandt L, Trägårdh E. Eur J Radiol 2019;113:89-95.