Diagnosing Heart Diseases with Deep Neural Networks
Joyce Park stashed this in Code
Long and technical but fascinating first-person account of building a proof-of-concept MRI reader that is significantly faster at diagnosing heart disease than a doctor.
Sounds like their proof of concept leaves a lot of room for improvement.
Still, this is a good problem to solve:
The goal of this year’s Data Science Bowl was to estimate minimum (end-systolic) and maximum (end-diastolic) volumes of the left ventricle from a set of MRI-images taken over one heartbeat. These volumes are used by practitioners to compute an ejection fraction: fraction of outbound blood pumped from the heart with each heartbeat. This measurement can predict a wide range of cardiac problems. For a skilled cardiologist analysis of MRI scans can take up to 20 minutes, therefore, making this process automatic is obviously useful.