From the post:
Hungry for more help? NVIDIA can feed your passion and fuel your progress.
The free course includes lecture recordings and hands-on exercises. You’ll learn how to design, train, and integrate neural network-powered artificial intelligence into your applications using widely-used open source frameworks and NVIDIA software.
Visit NVIDIA at: https://developer.nvidia.com/deep-learning-courses
For access to the hands-on labs for free, you’ll need to register, using the promo code KAGGLE, at: https://developer.nvidia.com/qwiklabs-signup
With weeks to go until the March 7 stage one deadline and stage two data release deadline, there’s still plenty of time for participants to take advantage of these tools and continue to submit solutions. Visit the Data Science Bowl Resources page for a complete listing of free resources.
If you aren’t already competing, the challenge in brief:
Declining cardiac function is a key indicator of heart disease. Doctors determine cardiac function by measuring end-systolic and end-diastolic volumes (i.e., the size of one chamber of the heart at the beginning and middle of each heartbeat), which are then used to derive the ejection fraction (EF). EF is the percentage of blood ejected from the left ventricle with each heartbeat. Both the volumes and the ejection fraction are predictive of heart disease. While a number of technologies can measure volumes or EF, Magnetic Resonance Imaging (MRI) is considered the gold standard test to accurately assess the heart’s squeezing ability.
The challenge with using MRI to measure cardiac volumes and derive ejection fraction, however, is that the process is manual and slow. A skilled cardiologist must analyze MRI scans to determine EF. The process can take up to 20 minutes to complete—time the cardiologist could be spending with his or her patients. Making this measurement process more efficient will enhance doctors’ ability to diagnose heart conditions early, and carries broad implications for advancing the science of heart disease treatment.
The 2015 Data Science Bowl challenges you to create an algorithm to automatically measure end-systolic and end-diastolic volumes in cardiac MRIs. You will examine MRI images from more than 1,000 patients. This data set was compiled by the National Institutes of Health and Children’s National Medical Center and is an order of magnitude larger than any cardiac MRI data set released previously. With it comes the opportunity for the data science community to take action to transform how we diagnose heart disease.
This is not an easy task, but together we can push the limits of what’s possible. We can give people the opportunity to spend more time with the ones they love, for longer than ever before. (From: https://www.kaggle.com/c/second-annual-data-science-bowl)
Unlike the servant with the one talent, Nvidia isn’t burying its talent under a basket. It is spreading access to its information as far as possible, in contrast to editorial writers at the New England Journal of Medicine.
Care to guess who is going to have the greater impact on cardiology and medicine?
I forgot to mention that Nietzsche described the editorial page writers of the New England Journal of Medicine quite well when he said, “…they tell the proper time and make a modest noise when doing so….” (Of Scholars).
I first saw this in a tweet by Kirk D. Borne.
Are you surprised that the data is dirty? 😉
I’m not a professional mathematicians but what if you created a common topology for hearts and then treated the different measurements for each one as dimensions?
I say that having recently read: Quantum algorithms for topological and geometric analysis of data by Seth Lloyd, Silvano Garnerone & Paolo Zanardi. Nature Communications 7, Article number: 10138 doi:10.1038/ncomms10138, Published 25 January 2016.
Whether you have a quantum computer or not, given the small size of the heart data set, some of those methods might be applicable.
Unless my memory fails me, the entire GPU Gems series in online at Nvidia and has several chapters on topological methods.