AViT: Adapting Vision Transformers for Small Skin Lesion Segmentation Datasets
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-47401-9_3
Reference42 articles.
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3. Ballerini, L., Fisher, R.B., Aldridge, B., Rees, J.: A color and texture based hierarchical K-NN approach to the classification of non-melanoma skin lesions. In: Celebi, M.E., Schaefer, G. (eds.) Color Medical Image Analysis, pp. 63–86. Springer Netherlands, Dordrecht (2013). https://doi.org/10.1007/978-94-007-5389-1_4
4. Birkenfeld, J.S., Tucker-Schwartz, J.M., et al.: Computer-aided classification of suspicious pigmented lesions using wide-field images. Comput. Methods Programs Biomed. 195, 105631 (2020)
5. Cao, H., et al.: Swin-Unet: Unet-Like Pure Transformer for Medical Image Segmentation. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds.) Computer Vision – ECCV 2022 Workshops: Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part III, pp. 205–218. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-25066-8_9
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