3.3k citations · 3.8k across the 9 of their papers we have counts for
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Surrogate Supervision for Medical Image Analysis: Effective Deep Learning From Limited Quantities of Labeled Data
Nima Tajbakhsh, Yufei Hu, Junli Cao +6
We investigate the effectiveness of a simple solution to the common problem of deep learning in medical image analysis with limited quantities of labeled training data. The underly…
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh +1
In this paper, we present UNet++, a new, more powerful architecture for medical image segmentation. Our architecture is essentially a deeply-supervised encoder-decoder network wher…
Automating Carotid Intima-Media Thickness Video Interpretation with Convolutional Neural Networks
Jae Y. Shin, Nima Tajbakhsh, R. Todd Hurst +2
Cardiovascular disease (CVD) is the leading cause of mortality yet largely preventable, but the key to prevention is to identify at-risk individuals before adverse events. For pred…
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
Nima Tajbakhsh, Jae Y. Shin, Suryakanth R. Gurudu +4
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 pro…