Stacked Autoencoders for Unsupervised Feature Learning and Multiple Organ Detection in a Pilot Study Using 4D Patient Data
Top Cited Papers
- 4 January 2013
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Pattern Analysis and Machine Intelligence
- Vol. 35 (8), 1930-1943
- https://doi.org/10.1109/tpami.2012.277
Abstract
Medical image analysis remains a challenging application area for artificial intelligence. When applying machine learning, obtaining ground-truth labels for supervised learning is more difficult than in many more common applications of machine learning. This is especially so for datasets with abnormalities, as tissue types and the shapes of the organs in these datasets differ widely. However, organ detection in such an abnormal dataset may have many promising potential real-world applications, such as automatic diagnosis, automated radiotherapy planning, and medical image retrieval, where new multimodal medical images provide more information about the imaged tissues for diagnosis. Here, we test the application of deep learning methods to organ identification in magnetic resonance medical images, with visual and temporal hierarchical features learned to categorize object classes from an unlabeled multimodal DCE-MRI dataset so that only a weakly supervised training is required for a classifier. A probabilistic patch-based method was employed for multiple organ detection, with the features learned from the deep learning model. This shows the potential of the deep learning model for application to medical images, despite the difficulty of obtaining libraries of correctly labeled training datasets and despite the intrinsic abnormalities present in patient datasets.This publication has 32 references indexed in Scilit:
- Unsupervised learning of hierarchical representations with convolutional deep belief networksCommunications of the ACM, 2011
- Optimizing functional parameter accuracy for breath-hold DCE-MRI of liver tumoursPhysics in Medicine & Biology, 2009
- Unsupervised Learning of Human Action Categories Using Spatial-Temporal WordsInternational Journal of Computer Vision, 2008
- A Fast Learning Algorithm for Deep Belief NetsNeural Computation, 2006
- Dynamic magnetic resonance imaging of tumor perfusionIEEE Engineering in Medicine and Biology Magazine, 2004
- Automatic tumor segmentation using knowledge-based techniquesIEEE Transactions on Medical Imaging, 1998
- Sparse coding with an overcomplete basis set: A strategy employed by V1?Vision Research, 1997
- On the limited memory BFGS method for large scale optimizationMathematical Programming, 1989
- Learning representations by back-propagating errorsNature, 1986
- On Information and SufficiencyThe Annals of Mathematical Statistics, 1951