Outside the conference venue at UMCG, Groningen. From left to right: Luigi Luppino, Fredrik Aspheim, Klara Leffler,Samuel Kuttner. Photo: Luigi Luppino
Fredrik Aspheim, Luigi Luppino, and Samuel Kuttner were co-authors of work that were awarded for best presentation at the Artificial Intelligence and Informatics in Nuclear Medicine Symposium Symposium (AINM) 2023.
Fredrik Aspheim, Luigi Luppino, and Samuel Kuttner were co-authors of work that were awarded for best presentation at the Artificial Intelligence and Informatics in Nuclear Medicine Symposium Symposium (AINM) 2023.
The Artificial Intelligence and Informatics in Nuclear Medicine (AINM) 2023 was arranged in Groningen, Netherlands during October 9-11 2023. It was the first international networking symposium of its kind, and gathered experts in the field of nuclear medicine, including clinicians, physicists, data scientists, students and industry partners, to discuss the current advancements, challenges, and the future of AI in nuclear medicine.
Luigi Luppino and Samuel Kuttner are principal investigators of the preclinical machine learning work package in the 180°N - Norwegian Nuclear Medicine Consortium (www.180N.no), and also associated to SFI Visual Intelligence, and UiT Machine learning group, participated with two submitted abstracts. One of the abstracts was the continuation of Fredrik Aspheim’s Master’s thesis, submitted earlier in 2023, and Aspheim was also co-authoring this AINM abstract. Furthermore, the second abstract was in collaboration with PhD student Klara Leffler and associate professor Jan Axelsson, from Umeå University. Both abstracts were accepted as oral presentations, and both oral presentations were awarded with best and second best presentation award, by an independent jury.
The two abstracts/presentations were:
● Interpretable deep learning model for input function estimation in small-animal 18F-FDG PET imaging, by Fredrik Aspheim, Luigi Tommaso Luppino, Michael Kampffmeyer, Robert Jenssen, Rune Sundset, and Samuel Kuttner, and
● Deep learning-based filling of incomplete sinograms from low-cost, long axial field-of-view PET scanners with inter-detector gaps, by Klara Leffler, Luigi Luppino, Samuel Kuttner, and Jan Axelsson.
The prices will allow the authors to submit their research at a reduced cost into an upcoming special issue of the open access journal Frontiers of Nuclear Medicine. The special issue is entitled “Nuclear Medicine Advances Through Artificial Intelligence and Intelligent Informatics”.
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