SLSNet: Weakly-Supervised Skin Lesion Segmentation Network with Self-attentions
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-7025-4_42
Reference15 articles.
1. Al-Huda, Z., Yao, Y., Yao, J., Peng, B., Raza, A.: Weakly supervised skin lesion segmentation based on spot-seeds guided optimal regions. IET Image Proc. 17(1), 239–255 (2023)
2. Al-Masni, M.A., Al-Antari, M.A., Choi, M.T., Han, S.M., Kim, T.S.: Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks. Comput. Methods Programs Biomed. 162, 221–231 (2018)
3. Bi, L., Kim, J., Ahn, E., Kumar, A., Feng, D., Fulham, M.: Step-wise integration of deep class-specific learning for dermoscopic image segmentation. Pattern Recogn. 85, 78–89 (2019)
4. Chang, Y.T., Wang, Q., Hung, W.C., Piramuthu, R., Tsai, Y.H., Yang, M.H.: Weakly-supervised semantic segmentation via sub-category exploration. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8991–9000 (2020)
5. Codella, N.C., et al.: Skin lesion analysis toward melanoma detection: a challenge at the 2017 international symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC). In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 168–172. IEEE (2018)
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