Osteologie, Inhaltsverzeichnis Osteologie 2021; 30(03): 261-263DOI: 10.1055/a-1534-3346 Gesellschaftsnachrichten Informationen der Arbeitsgemeinschaft Knochentumoren e. V. Artificial Intelligence (AI) for Radiological Diagnostics of Bone Tumors: Potential Approaches, Possibilities, and Limitations Claudio E. von Schacky Department of Radiology at Klinikum rechts der Isar, Technische Universität München, Ismaninger Str 22, 81675 Munich, Germany› InstitutsangabenArtikel empfehlen Abstract Artikel einzeln kaufen Volltext Referenzen 1 Choy G, Khalilzadeh O, Michalski M. et al. Current Applications and Future Impact of Machine Learning in Radiology. Radiology 2018; 288: 318-328 2 Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology 2016; 278: 563-577 3 Fletcher CDM. WHO Classification of Tumours of Soft Tissue and Bone; 4th ed.: World Health Organization; 2013 4 Paszke A, Gross S, Chintala S et al. Automatic differentiation in PyTorch. In: NIPS-W; 2017 5 Lalam R, Bloem JL, Noebauer-Huhmann IM. et al. ESSR Consensus Document for Detection, Characterization, and Referral Pathway for Tumors and Tumorlike Lesions of Bone. Seminars in musculoskeletal radiology. 2017; 21: 630-647
