anchor.diff:  <script type="text/javascript"> 
     (function defer() {
       if (window.jQuery) {      
  -      jQuery(document).ready(function() {      	
  +      jQuery(document).ready(function() {       
             disqus_shortname = 'raghakot-github-io';
             jQuery("p, img").inlineDisqussions();        
         });
  
positive.code: .Various ultrasound images overlayed with segmentation contours.
  image::ultrasound/contour.gif[contour]
  
  What makes this challenge particularly interesting is that ~60% of images do not contain the brachial plexus, i.e., segmentations mask is empty. Since the competetion uses the mean of link:https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient[dice coefficient] across all samples as the evaluation metric, even a single pixel in segmentation mask for non-nerve images mask kills the score. Ironically, you can achieve a score of ~0.6 just by predicting zero masks; it drops to ~[0.35, 0.45] when you try to learn it using a standard link:https://arxiv.org/pdf/1505.04597.pdf[U-Net]. This, combined with nosiy ultrasound images and a lack of obvious nerve patterns, intrigued me enough to participate in the challenge.
  
  > Unlike most kaggle posts, this article is more about the thought-process involved in coming up with a solution. I find it distasteful when folks just hand out a giant blob of code on github or just post their final complex solution that somehow gives good results.
  
  
negative.code: 
  == Step3: Real work begins...
  
  NOTE: This article is still a work in progress. Check back for updates.
  
  Since the beginning, it was clear that i had to do something about the weird score evaluation. Even a single pixel predicted on the segmentation output for non-nerve images was going to hurt the score. A pretty straight forward strategy was to add a `Dense` layer towards the end of encoder an predict whether the image contains the mask or not. Lets call this value `has_mask`
  
  During test/submission time, I could simply zero out all the segmentation mask values if `has_mask = False`.
  
  