Learning and Dimension Reduction in Medical Image AnalysisPP

We derive a numerical strategy for dimension reducing preprocessing to facilitate machine learning on high-dimensional imaging data. Certain orthogonal projectors are designed to reduce data dimension such that essential information content is preserved for subsequent learning. This strategy is applied to clinical spectral-domain optical coherence tomography data of the human retina for automated identification tasks.

This is poster number 20 in Poster Session

Anna Breger (University of Vienna)
deep learning, dimension reduction, image deblurring, image enhancement, image representation, image segmentation, machine learning