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Title:

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

Document type:
Journal Article
Author(s):
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...     »
Journal title abbreviation:
J Dtsch Dermatol Ges
Year:
2023
Journal volume:
21
Journal issue:
8
Pages contribution:
863-869
Fulltext / DOI:
doi:10.1111/ddg.15113
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/37306036
Print-ISSN:
1610-0379
TUM Institution:
Institut für KI und Informatik in der Medizin (Prof. Rückert); Klinik und Poliklinik für Dermatologie und Allergologie (Prof. Biedermann)
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