TY - GEN
T1 - Learning autoencoded radon projections
AU - Sriram, Aditya
AU - Kalra, Shivam
AU - Tizhoosh, H. R.
AU - Rahnamayan, Shahryar
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2018/2/2
Y1 - 2018/2/2
N2 - 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 ≈ 82% accuracy) outperforming state-of-the-art works on retrieval from IRMA dataset using autoencoders.
AB - 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 ≈ 82% accuracy) outperforming state-of-the-art works on retrieval from IRMA dataset using autoencoders.
UR - http://www.scopus.com/inward/record.url?scp=85046148079&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046148079&partnerID=8YFLogxK
U2 - 10.1109/SSCI.2017.8280803
DO - 10.1109/SSCI.2017.8280803
M3 - Conference contribution
AN - SCOPUS:85046148079
T3 - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
SP - 1
EP - 5
BT - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
Y2 - 27 November 2017 through 1 December 2017
ER -