| Title |
Melanoma multi class segmentation using different U-net type architectures / |
| Authors |
Dimša, Nojus ; Paulauskaitė-Tarasevičienė, Agnė |
| Full Text |
|
| Is Part of |
CEUR workshop proceedings: IVUS 2021: Information society and university studies 2021: Proceedings of the 26th international conference on information society and university studies (IVUS 2021), Kaunas, Lithuania, April 23, 2021 / edited by: I. Veitaitė, A. Lopata, T. Krilavičius, M. Woźniak.. Aachen : CEUR-WS. 2021, vol. 2915, art. no. 10, p. 84-91.. ISSN 1613-0073 |
| Keywords [eng] |
melanoma ; segmentation ; deep learning ; U-Ne ; image analysis |
| Abstract [eng] |
Automatic segmentation of skin lesions is the most important step towards the analysis of malignant melanoma, which is a specific kind of skin cancer. Deep learning is one of the most effective approaches to medical image processing applications. The encoder–decoder structures are good for segmentation tasks in particular the U-Net architecture, which is used as a basic architecture for the medical image segmentation networks. Recently, different variants of U-Net type architecture have been provided for improvement in terms of the segmentation results. Therefore, we focused on three U-Net type models, specifically U-Net, U-Net++ and MultiResUNet in order to evaluate their capability and performance on the multi class segmentation of melanoma. |
| Published |
Aachen : CEUR-WS |
| Type |
Conference paper |
| Language |
English |
| Publication date |
2021 |
| CC license |
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