Summary of Deep Implicit Optimization Enables Robust Learnable Features For Deformable Image Registration, by Rohit Jena et al.
Deep Implicit Optimization enables Robust Learnable Features for Deformable Image Registration
by Rohit Jena, Pratik Chaudhari, James C. Gee
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Deep Learning in Image Registration (DLIR) method aims to bridge the gap between statistical learning and optimization by incorporating optimization as a layer in a deep network. The approach uses a deep network to predict multi-scale dense feature images that are registered using an iterative optimization solver. By implicitly differentiating end-to-end through the optimization solver, the method exploits invariances of the correspondence matching problem induced by optimization, while learning registration and label-aware features. This framework demonstrates excellent performance on in-domain datasets and is agnostic to domain shift such as anisotropy and varying intensity profiles. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of registering images uses a deep network that helps solve this problem quickly and with good results even when there’s not much training data. This method combines the best parts of two different approaches: machine learning and optimization techniques. By using these together, it can handle different types of image registration tasks and even switch between them at test time without needing to retrain. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Optimization
