This collection shows an example of using unsupervised machine learning to predict heart disease using images derived from echocardiogram data (work done in conjunction with Paul Leeson and Ross Upton, University of Oxford).
The echocardiogram data is reduced from a 3D to a 2D image using a technique called principal strain analysis and the colours shown in the final image represent blood flow in the heart (dark = poor, red = medium, yellow = good). Using these data we can do Principal Component Analysis (PCA) to project each image into 2D space.
The collection explores image similarity comparison of oesophageal cancer video frames to find cancer lesions to train the ML model
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