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Classification/Dataset/dataset.zip

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+oid sha256:9e4075b7a1de2e065f32b9f23e7d861a4e2ba3ca0463d9f34a9dc8488a445406
+size 831405895

Classification/ReadMe/data_description.txt

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+Please refer to the following link for the description of the data and the code that accompanies the data.
+
+https://github.com/Harvard-Ophthalmology-AI-Lab/FairDomain
+
+The paper in which this dataset was first used and released:
+
+Tian, Y., Wen, C., Shi, M., Afzal, M.M., Huang, H., Khan, M.O., Luo, Y., Fang, Y. and Wang, M., 2024. FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classification. arXiv preprint arXiv:2407.08813.
+
+https://arxiv.org/pdf/2407.08813

Segmentation/Dataset/dataset.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7ea98f266b0bcfd2ad32b9fbdcd1459c11215ecd5b4695ecc7a40b656433a7b0
+size 5423426879

Segmentation/ReadMe/data_description.txt

@@ -0,0 +1,9 @@
+Please refer to the following link for the description of the data and the code that accompanies the data.
+
+https://github.com/Harvard-Ophthalmology-AI-Lab/FairDomain
+
+The paper in which this dataset was first used and released:
+
+Tian, Y., Wen, C., Shi, M., Afzal, M.M., Huang, H., Khan, M.O., Luo, Y., Fang, Y. and Wang, M., 2024. FairDomain: Achieving Fairness in Cross-Domain Medical Image Segmentation and Classification. arXiv preprint arXiv:2407.08813.
+
+https://arxiv.org/pdf/2407.08813