Mikulan E, Russo S, Zauli FM, Piergiorgio D, Parmigiani S, Favaro J, Knight W, Squarza S, Perri P, Cardinale F, Avanzini P, Pigorini A. A comparative study between state-of-the-art MRI deidentification and AnonyMI, a new method combining re-identification risk reduction and geometrical preservation.. [DOI: 10.1101/2021.07.30.454335][Cited by in Crossref: 1][Cited by in F6Publishing: 1][Article Influence: 0.5][Reference Citation Analysis]
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1
Sahlsten J, Wahid KA, Glerean E, Jaskari J, Naser MA, He R, Kann BH, Mäkitie A, Fuller CD, Kaski K. Segmentation stability of human head and neck cancer medical images for radiotherapy applications under de-identification conditions: Benchmarking data sharing and artificial intelligence use-cases. Front Oncol 2023;13:1120392. [PMID: 36925936 DOI: 10.3389/fonc.2023.1120392][Reference Citation Analysis]
2
Sahlsten J, Wahid KA, Glerean E, Jaskari J, Naser MA, He R, Fuller CD, Kaski K. Segmentation stability of human head and neck medical images for radiotherapy applications under de-identification conditions: benchmarking for data sharing and artificial intelligence use-cases.. [DOI: 10.1101/2022.01.22.22269695][Cited by in Crossref: 1][Cited by in F6Publishing: 1][Article Influence: 1.0][Reference Citation Analysis]
