Learning Autoencoded Radon Projections

TL;DR

This paper introduces a novel autoencoder-based framework for medical image retrieval that classifies Radon projections, demonstrating improved accuracy and faster training compared to existing methods on the IRMA dataset.

Contribution

The study presents a new autoencoder and MLP-based framework for classifying Radon projections, enhancing retrieval accuracy and training efficiency in medical imaging.

Findings

Achieved approximately 82% accuracy on IRMA dataset

Outperformed state-of-the-art autoencoder-based retrieval methods

Validated the effectiveness of Radon projections for image classification

Abstract

Autoencoders have been recently used for encoding medical images. In this study, we design and validate a new framework for retrieving medical images by classifying Radon projections, compressed in the deepest layer of an autoencoder. As the autoencoder reduces the dimensionality, a multilayer perceptron (MLP) can be employed to classify the images. The integration of MLP promotes a rather shallow learning architecture which makes the training faster. We conducted a comparative study to examine the capabilities of autoencoders for different inputs such as raw images, Histogram of Oriented Gradients (HOG) and normalized Radon projections. Our framework is benchmarked on IRMA dataset containing $14,410$ x-ray images distributed across $57$ different classes. Experiments show an IRMA error of $313$ (equivalent to $\approx 82\%$ accuracy) outperforming state-of-the-art works on retrieval from IRMA dataset using autoencoders.