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SPEMix: a lightweight method via superclass pseudo-label and efficient mixup for echocardiogram view classification
Shizhou Ma1, Yifeng Zhang2, Delong Li2
1College of Aulin, Northeast Forestry University, Harbin, China.
Insights
A new semi-supervised method, SPEMix, enhances echocardiogram view classification accuracy and generalization by effectively using unlabeled data. This approach improves diagnostic efficiency for cardiologists by enabling lightweight models for clinical applications.
Area of Science:
- Medical Imaging
- Artificial Intelligence in Medicine
- Cardiology
Background:
- Echocardiogram view classification is crucial for diagnosing heart diseases.
- Supervised methods struggle with generalization due to labeling difficulties.
- Semi-supervised methods face challenges with out-of-distribution data and complex models.
Purpose of the Study:
- To propose a novel open-set semi-supervised method (SPEMix) for echocardiogram view classification.
- To improve classification performance and generalization by leveraging out-of-distribution unlabeled data.
- To enable efficient clinical application with lightweight models.
Main Methods:
- Developed SPEMix with two core blocks: DAMix Block and SP Block.
- DAMix Block generates high-quality augmented echocardiograms using pixel-level masks.
- SP Block utilizes superclass probability distribution for pseudo-labeling unlabeled data.
Main Results:
- SPEMix improves classification accuracy by effectively utilizing unlabeled data.
- The method enhances generalization through superclass pseudo-labeling.
- A lightweight model trained with SPEMix achieved top performance on the TMED2 dataset.
Conclusions:
- SPEMix offers a robust solution for echocardiogram view classification, addressing limitations of current methods.
- The application of lightweight models in this domain facilitates clinical adoption.
- This approach aids cardiologists in more efficient and accurate heart disease diagnosis.
Introduction:
In clinical, the echocardiogram is the most widely used for diagnosing heart diseases. Different heart diseases are diagnosed based on different views of the echocardiogram images, so efficient echocardiogram view classification can help cardiologists diagnose heart disease rapidly. Echocardiogram view classification is mainly divided into supervised and semi-supervised methods. The supervised echocardiogram view classification methods have worse generalization performance due to the difficulty of labeling echocardiographic images, while the semi-supervised echocardiogram view classification can achieve acceptable results via a little labeled data. However, the current semi-supervised echocardiogram view classification faces challenges of declining accuracy due to out-of-distribution data and is constrained by complex model structures in clinical application.
Methods:
To deal with the above challenges, we proposed a novel open-set semi-supervised method for echocardiogram view classification, SPEMix, which can improve performance and generalization by leveraging out-of-distribution unlabeled data. Our SPEMix consists of two core blocks, DAMix Block and SP Block. DAMix Block can generate a mixed mask that focuses on the valuable regions of echocardiograms at the pixel level to generate high-quality augmented echocardiograms for unlabeled data, improving classification accuracy. SP Block can generate a superclass pseudo-label of unlabeled data from the perspective of the superclass probability distribution, improving the classification generalization by leveraging the superclass pseudolabel.
Results:
We also evaluate the generalization of our method on the Unity dataset and the CAMUS dataset. The lightweight model trained with SPEMix can achieve the best classification performance on the publicly available TMED2 dataset.
Discussion:
For the first time, we applied the lightweight model to the echocardiogram view classification, which can solve the limits of the clinical application due to the complex model architecture and help cardiologists diagnose heart diseases more efficiently.
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