Summary of An End-to-end Deep Learning Generative Framework For Refinable Shape Matching and Generation, by Soodeh Kalaie et al.
An End-to-End Deep Learning Generative Framework for Refinable Shape Matching and Generation
by Soodeh Kalaie, Andy Bulpitt, Alejandro F. Frangi, Ali Gooya
First submitted to arxiv on: 10 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 model generates realistic synthetic anatomical shapes by establishing refinable shape correspondences in a latent space, which is crucial for In-Silico Clinical Trials (ISCTs). A novel unsupervised geometric deep-learning model employs graph representations to address the challenges of variable vertex counts, connectivities, and lack of dense vertex-wise correspondences. The model constructs a population-derived atlas and generates realistic synthetic shapes, showcasing its applicability to computational medicine through experimental results using liver and left-ventricular models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new computer model can create lifelike copies of human body parts. This is important for testing medical devices in a virtual way, which saves time and money. The model uses graphs to connect the points on the shapes and establishes relationships between them. It creates an atlas, or a guide, that helps it generate realistic shapes. The model was tested using liver and heart models, and it worked well. |
Keywords
» Artificial intelligence » Deep learning » Latent space » Unsupervised
