Beothuk/dermamnist-federated

---

license: cc-by-nc-4.0
task_categories:
- image-classification
tags:
- medmnist
- medical-imaging
- skin-cancer
- federated-learning
- healthcare
size_categories:
- 10K<n<100K
---

# DermaMNIST Dataset for Federated Learning

## Dataset Description

**DermaMNIST** is a multi-class dataset of dermatoscopic images for skin lesion classification. It is part of the [MedMNIST v2](https://medmnist.com/) collection, a set of standardized biomedical image datasets for machine learning. This version of the dataset has been prepared for easy use in federated learning simulations, particularly with the [Flower](https://flower.dev/) framework.

The dataset is based on the [HAM10000](https://doi.org/10.1038/sdata.2018.161) dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. The images are 28x28 pixels and resized from their original 600x450 resolution.

### Classes
The dataset contains 7 classes of skin lesions:
- **0**: `actinic keratoses and intraepithelial carcinoma`

• 1: basal cell carcinoma

• 2: benign keratosis-like lesions

• 3: dermatofibroma

• 4: melanoma

• 5: melanocytic nevi

• 6: vascular lesions

  Dataset Statistics

  • Number of classes: 7
  • Training samples: 7,007
  • Test samples: 2,005
  • Total samples: 9,012

  Dataset Structure

  Data Fields

  • image: A PIL Image object of size 28x28.
  • label: The integer class ID.
  • label_name: The string name of the class label.

  Data Splits

  • Train: 7,007 samples
  • Test: 2,005 samples

  Usage with Flower Datasets 🌸

  This dataset is ideal for federated learning simulations where data is distributed across multiple clients (e.g., hospitals). Here's how to use it with Flower Datasets:

  from flwr_datasets import FederatedDataset
  
  # Define the number of clients (e.g., simulating 10 hospitals)
  NUM_CLIENTS = 10
  
  # Load the dataset and partition it into 10 clients
  fds = FederatedDataset(dataset="your-username/dermamnist-fl", partitioners={"train": NUM_CLIENTS})
  
  # Get the data for a specific client
  client_data = fds.load_partition(0, "train")
  
  # You can now use this client_data with your standard PyTorch or TensorFlow data loaders.
  # For example, to set the format for PyTorch:
  # client_data.set_format(type="torch", columns=["image", "label"])
  

  Citation

  If you use this dataset in your research, please cite the following paper:

  @article{medmnistv2,
      title={MedMNIST v2: A Large-Scale Lightweight Benchmark for 2D and 3D Biomedical Image Classification},
      author={Jiancheng Yang and Rui Shi and Donglai Wei and Zequan Liu and Lin Zhao and Bilian Ke and Hanspeter Pfister and Bingbing Ni},
      journal={Scientific Data},
      year={2023},
      volume={10},
      number={1},
      pages={41},
      doi={10.1038/s41597-022-01721-8}
  }
  

  Citation

      @article{medmnistv2,
          title={MedMNIST v2: A Large-Scale Lightweight Benchmark for 2D and 3D Biomedical Image Classification},
          author={Jiancheng Yang and Rui Shi and Donglai Wei and Zequan Liu and Lin Zhao and Bilian Ke and Hanspeter Pfister and Bingbing Ni},
          journal={Scientific Data},
          year={2023},
          volume={10},
          number={1},
          pages={41},
          doi={10.1038/s41597-022-01721-8}
      }
  

  Example Usage with datasets

  from datasets import load_dataset
  from PIL import Image
  
  # Load the dataset
  dataset = load_dataset("your-username/dermamnist-fl")
  
  # Access the training split
  train_data = dataset["train"]
  print(f"Number of training samples: {len(train_data)}")
  
  # Access a sample
  sample = train_data[100]
  image: Image = sample['image']
  print(f"Image size: {image.size}")
  print(f"Label: {sample['label']}")
  print(f"Label name: {sample['label_name']}")
  
  # To display the image
  # image.show()