Update README.md

README.md

@@ -11,11 +11,8 @@
 ### Dataset Description
 
 <!-- Provide a longer summary of what this dataset is. -->
-This dataset contains 18 variants, **PathMNIST**, **ChestMNIST**, **DermaMNIST**
 
-**Kuzushiji-MNIST** is a drop-in replacement for the MNIST dataset.
-
-**Kuzushiji-49**, as the name suggests, has 49 classes, is a much larger, but imbalanced dataset containing 48 Hiragana characters and one Hiragana iteration mark.
+MedMNIST is a large-scale MNIST-like collection of standardized biomedical images, including 12 datasets for 2D and 6 datasets for 3D. All images are pre-processed into 28x28 (2D) or 28x28x28 (3D) with the corresponding classification labels.
 
 - **License:** CC BY 4.0
 
@@ -23,41 +20,300 @@ This dataset contains 18 variants, **PathMNIST**, **ChestMNIST**, **DermaMNIST**
 
 <!-- Provide the basic links for the dataset. -->
 
-- **Homepage:** https://github.com/rois-codh/kmnist
-- **Paper:** Clanuwat, T., Bober-Irizar, M., Kitamoto, A., Lamb, A., Yamamoto, K., & Ha, D. (2018). Deep learning for classical japanese literature. arXiv preprint arXiv:1812.01718.
+- **Homepage:** https://medmnist.com/
+- **Paper:** Yang, J., Shi, R., Wei, D., Liu, Z., Zhao, L., Ke, B., ... & Ni, B. (2023). Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification. Scientific Data, 10(1), 41.
 
 ## Dataset Structure
 
 <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
 
-#### Kuzushiji-MNIST:
+#### PathMNIST:
+
+Total images: 107,180
+
+Classes: 9 categories
+
+Splits:
+
+- **Train:** 89,996 images
+
+- **Validation:** 10,004 images
+
+- **Test:** 7,180 images
+
+Image specs: 28×28 pixels
+
+#### ChestMNIST:
+
+Total images: 112,120
+
+Classes: 14 categories (multi-label)
+
+Splits:
+
+- **Train:** 78,468 images
+
+- **Validation:** 11,219 images
+
+- **Test:** 22,433 images
+
+Image specs: 28×28 pixels
+
+#### DermaMNIST:
+
+Total images: 10,015
+
+Classes: 7 categories
+
+Splits:
+
+- **Train:** 7,007 images
+
+- **Validation:** 1,003 images
+
+- **Test:** 2,005 images
+
+Image specs: 28×28 pixels
+
+#### OCTMNIST:
+
+Total images: 109,309
+
+Classes: 4 categories
+
+Splits:
+
+- **Train:** 97,477 images
+
+- **Validation:** 10,832 images
+
+- **Test:** 1,000 images
+
+Image specs: 28×28 pixels
+
+#### PneumoniaMNIST:
+
+Total images: 5,856
+
+Classes: 2 categories
+
+Splits:
+
+- **Train:** 4,708 images
+
+- **Validation:** 524 images
+
+- **Test:** 624 images
+
+Image specs: 28×28 pixels
+
+#### RetinaMNIST:
+
+Total images: 1,600
+
+Classes: 5 categories (ordinal regression)
+
+Splits:
+
+- **Train:** 1,080 images
+
+- **Validation:** 120 images
+
+- **Test:** 400 images
+
+Image specs: 28×28 pixels
+
+#### BreastMNIST:
+
+Total images: 780
+
+Classes: 2 categories
+
+Splits:
+
+- **Train:** 546 images
+
+- **Validation:** 78 images
+
+- **Test:** 156 images
+
+Image specs: 28×28 pixels
+
+#### BloodMNIST:
+
+Total images: 17,092
+
+Classes: 8 categories
+
+Splits:
+
+- **Train:** 11,959 images
+
+- **Validation:** 1,712 images
+
+- **Test:** 3,421 images
+
+Image specs: 28×28 pixels
+
+#### TissueMNIST:
 
-Total images: 70,000
+Total images: 236,386
 
-Classes: 10 categories
+Classes: 8 categories
 
 Splits:
 
-- **Train:** 60,000 images
+- **Train:** 165,466 images
+
+- **Validation:** 23,640 images
+
+- **Test:** 47,280 images
+
+Image specs: 28×28 pixels
+
+#### OrganAMNIST:
+
+Total images: 58,830
+
+Classes: 11 categories
+
+Splits:
+
+- **Train:** 34,561 images
+
+- **Validation:** 6,491 images
+
+- **Test:** 17,778 images
+
+Image specs: 28×28 pixels
+
+#### OrganCMNIST:
+
+Total images: 23,583
+
+Classes: 11 categories
+
+Splits:
+
+- **Train:** 12,975 images
+
+- **Validation:** 2,392 images
+
+- **Test:** 8,216 images
+
+Image specs: 28×28 pixels
+
+#### OrganSMNIST:
+
+Total images: 25,211
+
+Classes: 11 categories
+
+Splits:
+
+- **Train:** 13,932 images
+
+- **Validation:** 2,452 images
+
+- **Test:** 8,827 images
+
+Image specs: 28×28 pixels
+
+#### OrganMNIST3D:
+
+Total images: 1,742
+
+Classes: 11 categories
+
+Splits:
+
+- **Train:** 971 images
+
+- **Validation:** 161 images
+
+- **Test:** 610 images
+
+Image specs: 28×28x28 pixels
+
+#### NoduleMNIST3D:
+
+Total images: 1,633
+
+Classes: 2 categories
+
+Splits:
+
+- **Train:** 1,158 images
+
+- **Validation:** 165 images
+
+- **Test:** 310 images
+
+Image specs: 28×28x28 pixels
+
+#### AdrenalMNIST3D:
+
+Total images: 1,584
+
+Classes: 2 categories
+
+Splits:
+
+- **Train:** 1,188 images
+
+- **Validation:** 98 images
+
+- **Test:** 298 images
+
+Image specs: 28×28x28 pixels
+
+#### FractureMNIST3D:
+
+Total images: 1,370
+
+Classes: 3 categories
+
+Splits:
+
+- **Train:** 1,027 images
+
+- **Validation:** 103 images
+
+- **Test:** 240 images
+
+Image specs: 28×28x28 pixels
+
+#### VesselMNIST3D:
+
+Total images: 1,908
+
+Classes: 2 categories
+
+Splits:
+
+- **Train:** 1,335 images
+
+- **Validation:** 191 images
 
-- **Test:** 10,000 images
+- **Test:** 382 images
 
-Image specs: 28×28 pixels, grayscale
+Image specs: 28×28x28 pixels
 
-#### Kuzushiji-49:
+#### SynapseMNIST3D:
 
-Total images: 270,912
+Total images: 1,759
 
-Classes: 49 categories
+Classes: 2 categories
 
 Splits:
 
-- **Train:** 232,365 images
+- **Train:** 1,230 images
 
-- **Test:** 38,547 images
+- **Validation:** 177 images
 
-Image specs: 28×28 pixels, grayscale
+- **Test:** 352 images
 
+Image specs: 28×28x28 pixels
 
 ## Example Usage
 Below is a quick example of how to load this dataset via the Hugging Face Datasets library.
@@ -65,10 +321,24 @@ Below is a quick example of how to load this dataset via the Hugging Face Datase
 from datasets import load_dataset  
 
 # Load the dataset  
-dataset = load_dataset("randall-lab/kmnist", name="kmnist", split="train", trust_remote_code=True)   
-# dataset = load_dataset("randall-lab/kmnist", name="kmnist", split="test", trust_remote_code=True)
-# dataset = load_dataset("randall-lab/kmnist", name="k49mnist", split="train", trust_remote_code=True)   
-# dataset = load_dataset("randall-lab/kmnist", name="k49mnist", split="test", trust_remote_code=True)  
+dataset = load_dataset("randall-lab/medmnist", name="pathmnist", split="train", trust_remote_code=True)   
+# dataset = load_dataset("randall-lab/medmnist", name="chestmnist", split="train", trust_remote_code=True)
+# dataset = load_dataset("randall-lab/medmnist", name="dermamnist", split="train", trust_remote_code=True)   
+# dataset = load_dataset("randall-lab/medmnist", name="octmnist", split="train", trust_remote_code=True)  
+# dataset = load_dataset("randall-lab/medmnist", name="pneumoniamnist", split="train", trust_remote_code=True)  
+# dataset = load_dataset("randall-lab/medmnist", name="retinamnist", split="train", trust_remote_code=True)  
+# dataset = load_dataset("randall-lab/medmnist", name="breastmnist", split="train", trust_remote_code=True)  
+# dataset = load_dataset("randall-lab/medmnist", name="bloodmnist", split="train", trust_remote_code=True)  
+# dataset = load_dataset("randall-lab/medmnist", name="tissuemnist", split="train", trust_remote_code=True)  
+# dataset = load_dataset("randall-lab/medmnist", name="organamnist", split="train", trust_remote_code=True)  
+# dataset = load_dataset("randall-lab/medmnist", name="organcmnist", split="train", trust_remote_code=True)  
+# dataset = load_dataset("randall-lab/medmnist", name="organsmnist", split="train", trust_remote_code=True)  
+# dataset = load_dataset("randall-lab/medmnist", name="organmnist3d", split="train", trust_remote_code=True)  
+# dataset = load_dataset("randall-lab/medmnist", name="nodulemnist3d", split="train", trust_remote_code=True)  
+# dataset = load_dataset("randall-lab/medmnist", name="adrenalmnist3d", split="train", trust_remote_code=True)  
+# dataset = load_dataset("randall-lab/medmnist", name="fracturemnist3d", split="train", trust_remote_code=True)  
+# dataset = load_dataset("randall-lab/medmnist", name="vesselmnist3d", split="train", trust_remote_code=True)  
+# dataset = load_dataset("randall-lab/medmnist", name="synapsemnist3d", split="train", trust_remote_code=True)  
 
 # Access a sample from the dataset  
 example = dataset[0]  
@@ -85,9 +355,13 @@ print(f"Label: {label}")
 
 **BibTeX:**
 
-@article{clanuwat2018deep,
-  title={Deep learning for classical japanese literature},
-  author={Clanuwat, Tarin and Bober-Irizar, Mikel and Kitamoto, Asanobu and Lamb, Alex and Yamamoto, Kazuaki and Ha, David},
-  journal={arXiv preprint arXiv:1812.01718},
-  year={2018}
+@article{yang2023medmnist,
+  title={Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification},
+  author={Yang, Jiancheng and Shi, Rui and Wei, Donglai and Liu, Zequan and Zhao, Lin and Ke, Bilian and Pfister, Hanspeter and Ni, Bingbing},
+  journal={Scientific Data},
+  volume={10},
+  number={1},
+  pages={41},
+  year={2023},
+  publisher={Nature Publishing Group UK London}
 }