607 citations · 856 across the 5 of their papers we have counts for
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Unsupervised Joint Mining of Deep Features and Image Labels for Large-scale Radiology Image Categorization and Scene Recognition
Xiaosong Wang, Le Lu, Hoo-chang Shin +5
The recent rapid and tremendous success of deep convolutional neural networks (CNN) on many challenging computer vision tasks largely derives from the accessibility of the well-ann…
Unsupervised Category Discovery via Looped Deep Pseudo-Task Optimization Using a Large Scale Radiology Image Database
Xiaosong Wang, Le Lu, Hoo-chang Shin +4
Obtaining semantic labels on a large scale radiology image database (215,786 key images from 61,845 unique patients) is a prerequisite yet bottleneck to train highly effective deep…
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…
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…