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Over the last decade of machine learning, convolutional neural networks havebeen the most striking successes for feature extraction of rich sensory andhigh-dimensional data. While learning data representations via convolutions isalready well studied and efficiently implemented in various deep learninglibraries, one often faces limited memory capacity and insufficient number oftraining data, especially for high-dimensional and large-scale tasks. Toovercome these limitations, we introduce a network architecture using aself-adjusting and data dependent version of the Radon-transform (linear dataprojection), also known as x-ray projection, to enable feature extraction viaconvolutions in lower-dimensional space. The resulting framework, named PiNet,can be trained end-to-end and shows promising performance on volumetricsegmentation tasks. We test proposed model on public datasets to show that ourapproach achieves comparable results only using fractional amount ofparameters. Investigation of memory usage and processing time confirms PiNet'ssuperior efficiency compared to other segmentation models.

Deep Structure Learning using Feature Extraction in Trained Projection Space