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Titel:

Automatic body part identification in real-world clinical dermatological images using machine learning.

Dokumenttyp:
Journal Article
Autor(en):
Sitaru, Sebastian; Oueslati, Talel; Schielein, Maximilian C; Weis, Johanna; Kaczmarczyk, Robert; Rueckert, Daniel; Biedermann, Tilo; Zink, Alexander
Abstract:
BACKGROUND: Dermatological conditions are prevalent across all population sub-groups. The affected body part is of importance to their diagnosis, therapy, and research. The automatic identification of body parts in dermatological clinical pictures could therefore improve clinical care by providing additional information for clinical decision-making algorithms, discovering hard-to-treat areas, and research by identifying new patterns of disease. PATIENTS AND METHODS: In this study, we used 6,219...     »
Zeitschriftentitel:
J Dtsch Dermatol Ges
Jahr:
2023
Band / Volume:
21
Heft / Issue:
8
Seitenangaben Beitrag:
863-869
Volltext / DOI:
doi:10.1111/ddg.15113
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/37306036
Print-ISSN:
1610-0379
TUM Einrichtung:
Institut für KI und Informatik in der Medizin (Prof. Rückert); Klinik und Poliklinik für Dermatologie und Allergologie (Prof. Biedermann)
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