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--- |
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license: apache-2.0 |
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library_name: keras |
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tags: |
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- vision |
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- image-classification |
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--- |
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# Intro |
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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). |
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## Model description |
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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). |
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## Intended uses & limitations |
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You can use the raw model for melanoma detection from skin lesion images. |
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## How to use |
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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. |
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## Limitations and bias |
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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! |
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## Training data |
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See [dataset details](https://github.com/hasibzunair/adversarial-lesions#preparing-training-and-test-datasets). |
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## Training procedure |
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See [training details](https://github.com/hasibzunair/adversarial-lesions#training-both-stages). |
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## Evaluation results |
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For results in benchmarks, we refer to Figures 5, 6 and Table 1 of the original paper [here](https://arxiv.org/abs/2004.06824). |
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## Citation |
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```bibtex |
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@article{zunair2020melanoma, |
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title={Melanoma detection using adversarial training and deep transfer learning}, |
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author={Zunair, Hasib and Hamza, A Ben}, |
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journal={Physics in Medicine \& Biology}, |
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year={2020}, |
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publisher={IOP Publishing} |
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} |
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``` |
