anchor.diff:  image::ultrasound/contour.gif[contour]
   {empty} +
   
  -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 encoder-decoder link:https://arxiv.org/pdf/1411.4038.pdf[FCN]. This, combined with the fact that ultrasound images are noisy, intrigued me enough to participate in the challenge.
  +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: 
  Despite all this, the cleaned raining data manages to improve baseline model score to ~0.55.
  
  == Step3: Real work begins...
  
  
  
  