329 citations · 394 across the 10 of their papers we have counts for
4 papers · 1 filter
ErrorNet: Learning error representations from limited data to improve vascular segmentation
Nima Tajbakhsh, Brian Lai, Shilpa Ananth +1
Deep convolutional neural networks have proved effective in segmenting lesions and anatomies in various medical imaging modalities. However, in the presence of small sample size an…
Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation
Nima Tajbakhsh, Laura Jeyaseelan, Qian Li +3
The medical imaging literature has witnessed remarkable progress in high-performing segmentation models based on convolutional neural networks. Despite the new performance highs, t…
Automatic Segmentation of Pulmonary Lobes Using a Progressive Dense V-Network
Abdullah-Al-Zubaer Imran, Ali Hatamizadeh, Shilpa P. Ananth +3
Reliable and automatic segmentation of lung lobes is important for diagnosis, assessment, and quantification of pulmonary diseases. The existing techniques are prohibitively slow,…
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…