Summary of Multi-site Class-incremental Learning with Weighted Experts in Echocardiography, by Kit M. Bransby and Woo-jin Cho Kim and Jorge Oliveira and Alex Thorley and Arian Beqiri and Alberto Gomez and Agisilaos Chartsias
Multi-Site Class-Incremental Learning with Weighted Experts in Echocardiography
by Kit M. Bransby, Woo-jin Cho Kim, Jorge Oliveira, Alex Thorley, Arian Beqiri, Alberto Gomez, Agisilaos Chartsias
First submitted to arxiv on: 31 Jul 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 In a paper that tackles the challenge of building an echocardiography view classifier that maintains its performance in real-life cases, researchers propose a class-incremental learning method to address the issue of model drift. The approach involves learning an expert network for each dataset and combining them using a score fusion model. To minimize the influence of “unqualified experts,” the weights are learned in-distribution, promoting transparency during inference. Instead of using original images, the researchers utilize learned features from each dataset, which are easier to share and raise fewer licensing and privacy concerns. The method is validated on six datasets from multiple sites, demonstrating significant reductions in training time while improving view classification performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper aims to solve a problem in medicine where doctors use machines called echocardiography to take pictures of the heart. The pictures help doctors diagnose heart problems. The challenge is that the machine learning model that helps doctors with this task needs to be updated frequently to keep it accurate. The researchers found that simply updating the model doesn’t work because new data can make the model forget what it learned before. Instead, they propose a new way of updating the model that works well and is efficient. |
Keywords
* Artificial intelligence * Classification * Inference * Machine learning
