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DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation
Holger R. Roth, Le Lu, Amal Farag +4
Automatic organ segmentation is an important yet challenging problem for medical image analysis. The pancreas is an abdominal organ with very high anatomical variability. This inhi…
Interleaved Text/Image Deep Mining on a Large-Scale Radiology Database for Automated Image Interpretation
Hoo-Chang Shin, Le Lu, Lauren Kim +3
Despite tremendous progress in computer vision, there has not been an attempt for machine learning on very large-scale medical image databases. We present an interleaved text/image…
A Bottom-up Approach for Pancreas Segmentation using Cascaded Superpixels and (Deep) Image Patch Labeling
Amal Farag, Le Lu, Holger R. Roth +3
Robust automated organ segmentation is a prerequisite for computer-aided diagnosis (CAD), quantitative imaging analysis and surgical assistance. For high-variability organs such as…
Improving Computer-aided Detection using Convolutional Neural Networks and Random View Aggregation
Holger R. Roth, Le Lu, Jiamin Liu +5
Automated computer-aided detection (CADe) in medical imaging has been an important tool in clinical practice and research. State-of-the-art methods often show high sensitivities bu…
Anatomy-specific classification of medical images using deep convolutional nets
Holger R. Roth, Christopher T. Lee, Hoo-Chang Shin +5
Automated classification of human anatomy is an important prerequisite for many computer-aided diagnosis systems. The spatial complexity and variability of anatomy throughout the h…
Deep convolutional networks for pancreas segmentation in CT imaging
Holger R. Roth, Amal Farag, Le Lu +2
Automatic organ segmentation is an important prerequisite for many computer-aided diagnosis systems. The high anatomical variability of organs in the abdomen, such as the pancreas,…