Add references README

Author: nicolasreichardt

README.md

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 ## References
 
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+- Alsentzer, E., Li, M. M., Kobren, S. N., Noori, A., Undiagnosed Diseases Network, Kohane, I. S., & Zitnik, M. (2025). Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases. *npj Digital Medicine, 8*(1), 380. [https://doi.org/10.1038/s41746-025-01749-1](https://doi.org/10.1038/s41746-025-01749-1)
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+- Castello, R., Walch, A., Attias, R., Cadei, R., Jiang, S., & Scartezzini, J.-L. (2021). Quantification of the suitable rooftop area for solar panel installation from overhead imagery using convolutional neural networks. *Journal of Physics: Conference Series, 2042*(1), 012002. [https://doi.org/10.1088/1742-6596/2042/1/012002](https://doi.org/10.1088/1742-6596/2042/1/012002)
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+- Chen, Y., Wei, C., Wang, D., Ji, C., & Li, B. (2022). Semi-supervised contrastive learning for few-shot segmentation of remote sensing images. *Remote Sensing, 14*(17), 4254. [https://doi.org/10.3390/rs14174254](https://doi.org/10.3390/rs14174254)
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+- Ding, H., Zhang, H., & Jiang, X. (2022). Self-regularized prototypical network for few-shot semantic segmentation. *Pattern Recognition, 132*, 109018. [https://doi.org/10.1016/j.patcog.2022.109018](https://doi.org/10.1016/j.patcog.2022.109018)
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+- Finn, C., Abbeel, P., & Levine, S. (2017). Model-agnostic meta-learning for fast adaptation of deep networks. In *International Conference on Machine Learning* (pp. 1126–1135). PMLR. [https://doi.org/10.48550/arXiv.1703.03400](https://doi.org/10.48550/arXiv.1703.03400)
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+- Ge, Z., Fan, X., Zhang, J., & Jin, S. (2025). SegPPD-FS: Segmenting plant pests and diseases in the wild using few-shot learning. *Plant Phenomics*, 100121. [https://doi.org/10.1016/j.plaphe.2025.100121](https://doi.org/10.1016/j.plaphe.2025.100121)
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+- Hu, Y., Liu, C., Li, Z., Xu, J., Han, Z., & Guo, J. (2022). Few-shot building footprint shape classification with relation network. *ISPRS International Journal of Geo-Information, 11*(5), 311. [https://doi.org/10.3390/ijgi11050311](https://doi.org/10.3390/ijgi11050311)
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+- Jadon, S. (2021, February). COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approach. In *Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications* (Vol. 11601, pp. 161–170). SPIE. [https://doi.org/10.1117/12.2581496](https://doi.org/10.1117/12.2581496)
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+- Lee, G. Y., Dam, T., Ferdaus, M. M., Poenar, D. P., & Duong, V. (2025). Enhancing Few-Shot Classification of Benchmark and Disaster Imagery with ATTBHFA-Net. *arXiv preprint arXiv:2510.18326.* [https://doi.org/10.48550/arXiv.2510.18326](https://doi.org/10.48550/arXiv.2510.18326)
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+- Li, X., He, Z., Zhang, L., Guo, S., Hu, B., & Guo, K. (2025). CDCNet: Cross-domain few-shot learning with adaptive representation enhancement. *Pattern Recognition, 162*, 111382. [https://doi.org/10.1016/j.patcog.2025.111382](https://doi.org/10.1016/j.patcog.2025.111382)
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+- Puthumanaillam, G., & Verma, U. (2023). Texture based prototypical network for few-shot semantic segmentation of forest cover: Generalizing for different geographical regions. *Neurocomputing, 538*, 126201. [https://doi.org/10.1016/j.neucom.2023.03.062](https://doi.org/10.1016/j.neucom.2023.03.062)
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+- Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P. H., & Hospedales, T. M. (2018). Learning to compare: Relation network for few-shot learning. In *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition* (pp. 1199–1208). [https://doi.org/10.1109/CVPR.2018.00131](https://doi.org/10.1109/CVPR.2018.00131)
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