DocumentCode :
3254959
Title :
Autoencoder in Time-Series Analysis for Unsupervised Tissues Characterisation in a Large Unlabelled Medical Image Dataset
Author :
Shin, Hoo-Chang ; Orton, Matthew ; Collins, David J. ; Doran, Simon ; Leach, Martin O.
Author_Institution :
Inst. of Cancer Res., R. Marsden NHS Found. Trust, Sutton, UK
Volume :
1
fYear :
2011
fDate :
18-21 Dec. 2011
Firstpage :
259
Lastpage :
264
Abstract :
The topic of deep-learning has recently received considerable attention in the machine learning research community, having great potential to liberate computer scientists from hand-engineering training datasets, because the method can learn the desired features automatically. This is particularly beneficial in medical research applications of machine learning, where getting good hand labelling of data is especially expensive. We propose application of a single-layer sparse-auto encoder to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for fully automatic classification of tissue types in a large unlabelled dataset with minimal human interference -- in a manner similar to data-mining. DCE-MRI analysis, looking at the change of the MR contrast-agent concentration over successively acquired images, is time-series analysis. We analyse the change of brightness (which is related to the contrast-agent concentration) of the DCE-MRI images over time to classify different tissue types in the images. Therefore our system is an application of an auto encoder to time-series analysis while the demonstrated result and further possible successive application areas are in computer vision. We discuss the important factors affecting performance of the system in applying the auto encoder to the time-series analysis of DCE-MRI medical image data.
Keywords :
biological tissues; biomedical MRI; image classification; learning (artificial intelligence); medical image processing; time series; DCE-MRI analysis; computer vision; contrast-enhanced magnetic resonance imaging; fully automatic classification; large unlabelled medical image dataset; machine learning; medical research applications; single-layer sparse-autoencoder; time-series analysis; tissue types; unsupervised tissues characterisation; Cancer; Kidney; Liver; Medical diagnostic imaging; Training; Training data; DCE-MRI; autoencoder; deep-learning; liver; medical image analysis cancer; time-series analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Applications and Workshops (ICMLA), 2011 10th International Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
978-1-4577-2134-2
Type :
conf
DOI :
10.1109/ICMLA.2011.38
Filename :
6146980
Link To Document :
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