Spaces:

hasibzunair
/

melanoma-detection-demo

Build error

hasibzunair commited on Aug 14, 2022

Commit

e0b990e

1 Parent(s): 47f2f3e

update app

Files changed (1) hide show

description.html CHANGED Viewed

@@ -6,18 +6,15 @@
 </head>
 <body>
     This is a demo of <a href="https://arxiv.org/abs/2004.06824">Melanoma Detection using Adversarial Training and Deep Transfer Learning</a> (Physics in Medicine and Biology, 2020).</br>
     We introduce an over-sampling method for learning the inter-class mapping between under-represented
     class samples and over-represented samples in a bid to generate under-represented class samples
     using unpaired image-to-image translation. These synthetic images are then used as additional
     training data in the task of detecting abnormalities in binary classification use-cases.
-    Code is publicly available in <a href='https://github.com/hasibzunair/adversarial-lesions'>Github</a>.</br>
     This method was also effective for COVID-19 detection from chest radiography images which led to
     <a href="https://github.com/hasibzunair/synthetic-covid-cxr-dataset">Synthetic COVID-19 Chest X-ray Dataset for Computer-Aided Diagnosis</a>.
     The synthetic images not only improved performance of various deep learning architectures when used as additional training data
     under heavy imbalance conditions, but also detect the target class (e.g. COVID-19) with high confidence. </br>
     This model predicts if the given image has benign or malignant symptoms.
     To use it, simply upload a skin lesion image, or click one of the examples to load them.
     Read more at the links below.

 </head>
 <body>
     This is a demo of <a href="https://arxiv.org/abs/2004.06824">Melanoma Detection using Adversarial Training and Deep Transfer Learning</a> (Physics in Medicine and Biology, 2020).</br>
     We introduce an over-sampling method for learning the inter-class mapping between under-represented
     class samples and over-represented samples in a bid to generate under-represented class samples
     using unpaired image-to-image translation. These synthetic images are then used as additional
     training data in the task of detecting abnormalities in binary classification use-cases.
+    Code is publicly available in <a href='https://github.com/hasibzunair/adversarial-lesions'>Github</a>.</br></br>
     This method was also effective for COVID-19 detection from chest radiography images which led to
     <a href="https://github.com/hasibzunair/synthetic-covid-cxr-dataset">Synthetic COVID-19 Chest X-ray Dataset for Computer-Aided Diagnosis</a>.
     The synthetic images not only improved performance of various deep learning architectures when used as additional training data
     under heavy imbalance conditions, but also detect the target class (e.g. COVID-19) with high confidence. </br>
     This model predicts if the given image has benign or malignant symptoms.
     To use it, simply upload a skin lesion image, or click one of the examples to load them.
     Read more at the links below.