1. Akrout, M., et al.: Diffusion-based data augmentation for skin disease classification: Impact across original medical datasets to fully synthetic images. In: Mukhopadhyay, A., Oksuz, I., Engelhardt, S., Zhu, D., Yuan, Y. (eds.) MICCAI 202. LNCS, pp. 99–109. Springer, Heidelberg (2023). https://doi.org/10.1007/978-3-031-53767-7_10
2. Azad, R., Heidari, M., Wu, Y., Merhof, D.: Contextual attention network: transformer meets u-net. In: Lian, C., Cao, X., Rekik, I., Xu, X., Cui, Z. (eds.) MLMI 2022. LNCS, pp. 377–386. Springer, Heidelberg (2022). https://doi.org/10.1007/978-3-031-21014-3_39
3. Badrinarayanan, V., Handa, A., Cipolla, R.: Segnet: a deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling. Comput. Sci. (2015)
4. Baid, U., et al.: The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification. arXiv preprint arXiv:2107.02314 (2021)
5. Chen, J., Lu, Y., Yu, Q., Luo, X., Zhou, Y.: Transunet: transformers make strong encoders for medical image segmentation (2021)