update readme

README.md

@@ -6,32 +6,26 @@ tags:
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 # Intro
-
-This is the model for our paper "Melanoma Detection using Adversarial Training and Deep Transfer Learning".
+This is the model for our paper ["Melanoma Detection using Adversarial Training and Deep Transfer Learning"](https://arxiv.org/abs/2004.06824).
 
 ## 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). Training details are [here](https://github.com/hasibzunair/adversarial-lesions).
 
 ## 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://huggingface.co/spaces/hasibzunair/melanoma-detection-demo) deploy section.
+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 [details](https://github.com/hasibzunair/adversarial-lesions#preparing-training-and-test-datasets).
+See [dataset details](https://github.com/hasibzunair/adversarial-lesions#preparing-training-and-test-datasets).
 
 ## Training procedure
 
-See [details](https://github.com/hasibzunair/adversarial-lesions#training-both-stages). 
+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).