Multiscale distribution preserving autoencoders for plaque detection in intravascular optical coherence tomography

Abstract

Interventional cardiologists use intravascular imaging techniques like optical coherence tomography (OCT) as adjunct to angiography for detailed diagnosis of atherosclerosis. Each tissue type is associated with characteristic speckle intensity distribution, which forms the basis for tissue characterization (TC). Classical approaches follow statistical machine learning using apriori assumed speckle models, and are challenged by inability to discriminate high tissue heterogeneity. As a first of its kind approach, we solve this problem in absence of a well studied distribution, by learning the multiscale statistical distribution model of the data using our proposed distribution preserving (DP) autoencoder (AE) based neural network (NN). The learning rule introduces a scale importance parameter associated with error backpropagation. We have evaluated performance of DPAE vs. prior-art and AE (with L2 norm and cross-entropy cost function) to obtain LogLoss of 0.16, 0.28, 0.22, 0.53 respectively, and 93.6% average classification accuracy with DPAE predictions were judged to be clinically acceptable.