Search Results for author: Jae Y. Shin

Found 3 papers, 1 papers with code

Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation Efforts

1 code implementation3 Feb 2018 Zongwei Zhou, Jae Y. Shin, Suryakanth R. Gurudu, Michael B. Gotway, Jianming Liang

The splendid success of convolutional neural networks (CNNs) in computer vision is largely attributable to the availability of massive annotated datasets, such as ImageNet and Places.

Active Learning Transfer Learning

Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?

no code implementations2 Jun 2017 Nima Tajbakhsh, Jae Y. Shin, Suryakanth R. Gurudu, R. Todd Hurst, Christopher B. Kendall, Michael B. Gotway, Jianming Liang

Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence.

Transfer Learning

Automating Carotid Intima-Media Thickness Video Interpretation with Convolutional Neural Networks

no code implementations CVPR 2016 Jae Y. Shin, Nima Tajbakhsh, R. Todd Hurst, Christopher B. Kendall, Jianming Liang

However, each CIMT examination includes several ultrasound videos, and interpreting each of these CIMT videos involves three operations: (1) select three end-diastolic ultrasound frames (EUF) in the video, (2) localize a region of interest (ROI) in each selected frame, and (3) trace the lumen-intima interface and the media-adventitia interface in each ROI to measure CIMT.

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