
# 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}
}
```
{"license": "apache-2.0", "library_name": "keras", "tags": ["vision", "image-classification"]}
keras vision image-classification arxiv:2004.06824 license:apache-2.0 has_space region:us
TAGS
#keras #vision #image-classification #arxiv-2004.06824 #license-apache-2.0 #has_space #region-us 


# Intro
This is the model for our paper "Melanoma Detection using Adversarial Training and Deep Transfer Learning". Code is available here.

## Model description
The model is trained on the ISIC 2016 Task 3 dataset. The architecture and algorithm is described in this paper.

## Intended uses & limitations
You can use the raw model for melanoma detection from skin lesion images.

## How to use
See Spaces demo. For more code examples, we refer to this GitHub 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.

## Training procedure
See training details. 

## Evaluation results
For results in benchmarks, we refer to Figures 5, 6 and Table 1 of the original paper here.



# Intro
This is the model for our paper "Melanoma Detection using Adversarial Training and Deep Transfer Learning". Code is available here. ## Model description
The model is trained on the ISIC 2016 Task 3 dataset. The architecture and algorithm is described in this paper. ## Intended uses & limitations
You can use the raw model for melanoma detection from skin lesion images. ## How to use
See Spaces demo. For more code examples, we refer to this GitHub 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. ## Training procedure
See training details. ## Evaluation results
For results in benchmarks, we refer to Figures 5, 6 and Table 1 of the original paper here.
TAGS
#keras #vision #image-classification #arxiv-2004.06824 #license-apache-2.0 #has_space #region-us 
 # Intro
This is the model for our paper "Melanoma Detection using Adversarial Training and Deep Transfer Learning". Code is available here. ## Model description
The model is trained on the ISIC 2016 Task 3 dataset. The architecture and algorithm is described in this paper. ## Intended uses & limitations
You can use the raw model for melanoma detection from skin lesion images. ## How to use
See Spaces demo. For more code examples, we refer to this GitHub 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. ## Training procedure
See training details. ## Evaluation results
For results in benchmarks, we refer to Figures 5, 6 and Table 1 of the original paper here.
passage: TAGS
#keras #vision #image-classification #arxiv-2004.06824 #license-apache-2.0 #has_space #region-us 
# Intro
This is the model for our paper "Melanoma Detection using Adversarial Training and Deep Transfer Learning". Code is available here.## Model description
The model is trained on the ISIC 2016 Task 3 dataset. The architecture and algorithm is described in this paper.## Intended uses & limitations
You can use the raw model for melanoma detection from skin lesion images.## How to use
See Spaces demo. For more code examples, we refer to this GitHub 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.## Training procedure
See training details.## Evaluation results
For results in benchmarks, we refer to Figures 5, 6 and Table 1 of the original paper here.