Summary of Dosegnn: Improving the Performance Of Deep Learning Models in Adaptive Dose-volume Histogram Prediction Through Graph Neural Networks, by Zehao Dong et al.
DoseGNN: Improving the Performance of Deep Learning Models in Adaptive Dose-Volume Histogram Prediction through Graph Neural Networks
by Zehao Dong, Yixin Chen, Tianyu Zhao
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper focuses on developing efficient deep learning models for predicting Dose-Volume Histograms (DVHs) in radiation therapy. The goal is to create a plug-and-play framework that improves predictive performance on general radiotherapy platforms equipped with high-performance CBCT systems. The authors evaluate widely-used deep learning models on this platform and find that graph neural networks (GNNs) are the most effective architecture for this task, particularly in adaptive settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make radiation therapy more precise by predicting how much radiation will reach different parts of the body. It uses special machines called CBCT scanners to take pictures of the body and then uses computers to predict where the radiation will go. The authors tested different ways of using these machines and found that a type of computer model called graph neural networks works best. This can help doctors give patients the right amount of radiation while also keeping healthy tissues safe. |
Keywords
* Artificial intelligence * Deep learning
