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Erschienen in:
2024 | OriginalPaper | Buchkapitel
Advanced Deep Learning for Skin Histoglyphics at Cellular Level
verfasst von : Robert Kreher, Naveeth Reddy Chitti, Georg Hille, Janine Hürtgen, Miriam Mengonie, Andreas Braun, Thomas Tüting, Bernhard Preim, Sylvia Saalfeld
Erschienen in: Bildverarbeitung für die Medizin 2024
Verlag: Springer Fachmedien Wiesbaden
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In dermatology, the histological examination of skin cross-sections is essential for skin cancer diagnosis and treatment planning. However, the complete coverage of tissue abnormalities is not possible due to time constraints as well as the sheer number of cell groups. We present an automatic segmentation approach of seven tissue classes: vessels, perspiration glands, hair follicles, sebaceous glands, tumor tissue, epidermis and fatty tissue, for a fast processing of the large datasets. Hence, the initial size of the data lends itself to the use of patch-based deep learning models, resulting in good IoU score of 94.2 percent for the cancerous tissue and overall IoU score of 83.6 percent.