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Code for running deep learning on 2D projections from 3D volumes
- Project
- 20044 ASSIST
- Type
- New library
- Description
Deep learning on large 3D medical volumes is very computationally demanding. We have demonstrated that using 2D projections from 3D volumes can lead to reasonable performance (for brain age prediction from MRI volumes), with a much lower computational complexity. We share Julia code for running 2D CNNs with projections as input channels.
- Contact
- Anders Eklund
- anders.eklund@liu.se
- Research area(s)
- Deep learning, medical imaging
- Technical features
The Julia code performs training of 2D convolutional neural networks (CNN) with different projections as input channels, to perform classification or regression.
- Integration constraints
The code is written in the Julia programming language
- Targeted customer(s)
Anyone using deep learning on 3D volumes
- Conditions for reuse
See the GitHub repository
- Confidentiality
- Public
- Publication date
- 06-09-2024
- Involved partners
- Linköping University (SWE)
