Abstract
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.
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© 2024 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Kreher, R. et al. (2024). Advanced Deep Learning for Skin Histoglyphics at Cellular Level. In: Maier, A., Deserno, T.M., Handels, H., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2024. BVM 2024. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-44037-4_20
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DOI: https://doi.org/10.1007/978-3-658-44037-4_20
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