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Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learning.
Zhe Huang1, Benjamin S Wessler2, Michael C Hughes1
1Dept. of Computer Science, Tufts University, Medford, MA, USA.
Proceedings of Machine Learning Research
|March 11, 2024
Summary
This study introduces an improved deep learning method for diagnosing aortic stenosis (AS) from echocardiograms. The new approach enhances accuracy and reduces model size by focusing on relevant ultrasound views and using a novel pretraining strategy.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Aortic stenosis (AS) is a serious heart valve condition with significant health consequences.
- Current diagnosis relies on expert interpretation of echocardiograms, which is time-consuming and prone to under-diagnosis.
- Automating AS screening using deep learning requires identifying relevant views and aggregating information for accurate diagnosis.
Purpose of the Study:
- To develop an automated deep learning system for accurate aortic stenosis screening.
- To improve upon existing methods that struggle with image selection and aggregation in echocardiography.
- To enhance the efficiency and accuracy of AS diagnosis through novel artificial intelligence techniques.
Main Methods:
- Developed a novel end-to-end multiple instance learning (MIL) approach for AS detection.
- Implemented a supervised attention mechanism to focus on diagnostically relevant echocardiographic views.
- Utilized a self-supervised pretraining strategy with contrastive learning on the entire study representation.
Main Results:
- The proposed MIL approach demonstrated higher accuracy in AS detection compared to previous methods.
- The new technique effectively identified and utilized relevant echocardiographic views for diagnosis.
- The model achieved improved performance while also reducing overall model size.
Conclusions:
- The novel end-to-end MIL approach with supervised attention and self-supervised pretraining significantly improves automated aortic stenosis detection.
- This method offers a more accurate and efficient alternative to current diagnostic practices for AS.
- The findings suggest a promising direction for AI-driven cardiovascular diagnostics.
