BSP-Net: automatic skin lesion segmentation improved by boundary enhancement and progressive decoding methods-Reference-Cited by-同舟云学术

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BSP-Net: automatic skin lesion segmentation improved by boundary enhancement and progressive decoding methods

Published:2024-08-29 Issue:5 Volume:30 Page:
ISSN:0942-4962
Container-title:Multimedia Systems
language:en
Short-container-title:Multimedia Systems

Author:

Ma Chengyun,Yang Qimeng,Tian Shengwei,Yu Long,Yu Shirong

Funder

National Natural Science Foundation of China

Tianshan Talent Training Program

Xinjiang Uygur Autonomous Region Key R & D program

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00530-024-01453-2.pdf

Reference51 articles.

1. Celebi, M.E., Kingravi, H.A., Uddin, B., Iyatomi, H., Aslandogan, Y.A., Stoecker, W.V., Moss, R.H.: A methodological approach to the classification of dermoscopy images. Comput. Med. Imaging Graph. 31(6), 362–373 (2007)

2. Yueksel, M.E., Borlu, M.: Accurate segmentation of dermoscopic images by image thresholding based on type-2 fuzzy logic. IEEE Trans. Fuzzy Syst. 17(4), 976–982 (2009)

3. Schmid, P.: Segmentation of digitized dermatoscopic images by two-dimensional color clustering. IEEE Trans. Med. Imaging 18(2), 164–171 (1999)

4. Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, Part III 18, pp. 234–241 (2015). Springer

5. Gao, J., Zhang, T., Xu, C.: Learning to model relationships for zero-shot video classification. IEEE Trans. Pattern Anal. Mach. Intell. 43(10), 3476–3491 (2020)

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