Deep learning in medical imaging

  1. If you want to quickly understand the fundamental concepts, we strongly advice to check our blog post, which provides a high level overview of all the aspects of medical image segmentation and deep learning.
  2. Another more introductory article I wrote on medical imaging concepts for deep learning can be found here.
  3. For more background in Deep Learning in MRI please advice this.
  4. Our open source library Medical Zoo pytorch is provided in the Github link, which implements the following architectures: U-Net3D,V-net, ResNet3D-VAE, SkipDesneNet3D, HyperDense-Net, multi-stream Densenet3D, DenseVoxelNet, MED3D, HighResNet3D

Don't forget to tag @black0017 in your comment, otherwise they may not be notified.

Authors original post
Deep Learning Researcher on Computer Vision and Medical Imaging
Share this project
Similar projects
AI in Medicine and Imaging - Stanford Symposium 2020
Through the AIMI Symposium we hope to address gaps and barriers in the field and catalyze more evidence-based solutions to improve health for all.
Semixup: In- and Out-of-Manifold Regularization
Semixup is a semi-supervised learning method based on in/out-of-manifold regularization.
A comprehensive healthcare conversational agent powered by Visual QA and segmentation models.
Stochastic Segmentation Networks
An efficient probabilistic method for modelling aleatoric uncertainty with any image segmentation network architecture.
Top collections