--- 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} } ```