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Segmentation Sample Models/YNet/README.md

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 # YNet
 ## Dataset 
 The dataset used is available in [this](https://www.bcsc-research.org/data/variables/) post.
-A total of 87 pathologists diagnosed a randomly assigned subset of 60 slides into four diagnostic categories (benign, atypia, ductal carcinomain situ, and invasive cancer),producing an average of 22 diagnostic labels per case.  The average size of these ROIs is10,000×12,000. We use these 200 ROIs for our data set.
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+A total of 87 pathologists diagnosed a randomly assigned subset of 60 slides into four diagnostic categories producing an average of 22 diagnostic labels per case.  The average size of these ROIs is 10,000×12,000. Out of these 200 ROIs have been used.
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+The four diagnostic categories are : i) benign ii) atypia  iii) ductal carcinomain situ iv) invasive cancer
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+Patch Size used : 256 X 256
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+Image Format : RGB
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+Pre-Processing : 256 X 256 patches are cropped from the high redolution ( 10000 X 12000 ) images without any overlap. As there is no seperate test dataset 15% of the extracted patches are kept aside for testing and the rest are used for training. Pixel values are normalized before training.
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+Magnification : 100x
 
 ## Model
 [Y-net](https://arxiv.org/pdf/1806.01313.pdf): 
 
 ![model architecture](ynet.png)
 
 
-Input image size: 256 x 256 x 3
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 Citation