Summary of Coreecho: Continuous Representation Learning For 2d+time Echocardiography Analysis, by Fadillah Adamsyah Maani et al.
CoReEcho: Continuous Representation Learning for 2D+time Echocardiography Analysis
by Fadillah Adamsyah Maani, Numan Saeed, Aleksandr Matsun, Mohammad Yaqub
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 Deep learning models have revolutionized automatic medical image analysis, particularly in echocardiography. This paper presents a novel training framework called CoReEcho that enables direct ejection fraction (EF) regression from 2D+time echocardiograms with superior performance. Unlike traditional end-to-end training pipelines, CoReEcho focuses on continuous representations to mitigate the issue of spurious correlations and improve generalization. The proposed framework outperforms the current state-of-the-art on the largest echocardiography dataset (EchoNet-Dynamic) with a mean absolute error (MAE) of 3.90 and R-squared value of 82.44. Additionally, CoReEcho provides robust and transferable features that can be applied to related downstream tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CoReEcho is a new way to train deep learning models for medical image analysis. It helps computers analyze ultrasound images of the heart and figure out how well the heart is working. This is important because doctors need this information to take care of patients with heart problems. The old way of training these models was not very good, so the scientists in this paper came up with a new idea called CoReEcho. It works better than the old way and can even help with other tasks related to medical imaging. |
Keywords
* Artificial intelligence * Deep learning * Generalization * Mae * Regression
