In our last blog post we described an end-to-end deep learning solution to this challenge. By “end-to-end” we mean that the raw pixels constituting a SAX study for an individual patient were fed into a convolutional neural network (ConvNet) and predicted left ventricle (LV) systolic and diastolic CDFs came out the other end - the only other processing that took place was the zero mean unit variance (ZMUV) pre-processing of the images. Whilst this approach to the problem is elegant in its simplicity, it is also a very challenging function for a neural network to learn. This is because there is no explicit training signal for the area of the left ventricle that should be measured from each image, just the whole volume for the SAX study. Read More

We just made a contest submission scoring 0.022886, placing us 14th on the leaderboard as of February 2nd. In this post we’ll outline our current approach and some of the challenges we think we’ll face as we try to further improve our performance. Read More