readme: ---
  license: apache-2.0
  library_name: keras
  tags:
  - vision
  - image-classification
  ---
  
  # Intro
  This is the model for our paper ["Melanoma Detection using Adversarial Training and Deep Transfer Learning"](https://arxiv.org/abs/2004.06824). Code is available [here](https://github.com/hasibzunair/adversarial-lesions).
  
  ## Model description
  The model is trained on the ISIC 2016 Task 3 dataset. The architecture and algorithm is described in this [paper](https://arxiv.org/abs/2004.06824).
  
  ## Intended uses & limitations
  You can use the raw model for melanoma detection from skin lesion images.
  
  ## How to use
  See Spaces [demo](https://huggingface.co/spaces/hasibzunair/melanoma-detection-demo). For more code examples, we refer to this [GitHub](https://github.com/hasibzunair/adversarial-lesions#deploy) deploy section.
  
  ## Limitations and bias
  The model is trained on a specific dataset with just over a thousand samples. It may or may not work for other kinds of skin lesion images. Further, there is no out-of-distribution detection method to filter out non skin lesion images. If you give an image of a dog, the model will still classify it as benign for malignant! 
  
  ## Training data
  See [dataset details](https://github.com/hasibzunair/adversarial-lesions#preparing-training-and-test-datasets).
  
  ## Training procedure
  See [training details](https://github.com/hasibzunair/adversarial-lesions#training-both-stages). 
  
  ## Evaluation results
  For results in benchmarks, we refer to Figures 5, 6 and Table 1 of the original paper [here](https://arxiv.org/abs/2004.06824).
  
  
  ## Citation
  ```bibtex
  @article{zunair2020melanoma,
    title={Melanoma detection using adversarial training and deep transfer learning},
    author={Zunair, Hasib and Hamza, A Ben},
    journal={Physics in Medicine \& Biology},
    year={2020},
    publisher={IOP Publishing}
  }
  ```