Stacked Autoencoders for Unsupervised Feature Learning and Multiple Organ Detection in a Pilot Study Using 4D Patient Data
Citations
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Cites methods from "Stacked Autoencoders for Unsupervis..."
...(51) used SAEs to separately learn both visual and temporal features in order to detect multiple organs in a time series of 3D dynamic contrast–enhanced MRI scans over data sets from two studies of liver metastases and one study of kidney metastases....
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2,306 citations
Cites methods from "Stacked Autoencoders for Unsupervis..."
...It has been applied with success in classification tasks, natural language processing, dimensionality reduction, object detection, motion modeling, and so on [5]–[9]....
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1,970 citations
Cites background from "Stacked Autoencoders for Unsupervis..."
...Finally, [74] leverages stacked autoencoders for multiple organ detection in medical images, while [75] exploits saliency-guided stacked autoencoders for videobased salient object detection....
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References
31,952 citations
"Stacked Autoencoders for Unsupervis..." refers background in this paper
...Index Terms—Edge and feature detection, object recognition, pixel classification, machine learning, biomedical image processing Ç...
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...This is especially so for datasets with abnormalities, as tissue types and the shapes of the organs in these datasets differ widely....
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...Our aim here is to solve these problems by using deep architectures with spatial-pooling so that feature learning can incorporate progressively larger spatial regions....
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23,814 citations
"Stacked Autoencoders for Unsupervis..." refers background in this paper
...The abdominal region contains many important organs and therefore has great potential to be useful for automated diagnosis and radiotherapy planning....
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16,989 citations
"Stacked Autoencoders for Unsupervis..." refers background in this paper
...This is especially so for datasets with abnormalities, as tissue types and the shapes of the organs in these datasets differ widely....
[...]
...Our aim here is to solve these problems by using deep architectures with spatial-pooling so that feature learning can incorporate progressively larger spatial regions....
[...]