Publications by authors named "Ching-Hsun Tseng"
Real-Time Automatic M-Mode Echocardiography Measurement With Panel Attention.
IEEE J Biomed Health Inform
September 2024
Motion mode (M-mode) echocardiography is essential for measuring cardiac dimension and ejection fraction. However, the current diagnosis is time-consuming and suffers from diagnosis accuracy variance. This work resorts to building an automatic scheme through well-designed and well-trained deep learning to conquer the situation.
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- The text discusses advancements in computer vision, highlighting the popularity of deep convolutional neural networks (CNNs) and the challenge of increased resource demand when stacking layers.
- It addresses the limitations of resource-intensive models in scenarios with constrained hardware and proposes a new architecture called Universal Pixel Attention Networks (UPANets) that enhances performance efficiently.
- UPANets incorporate channel and spatial direction attention mechanisms, allowing them to learn global information with lower resource consumption while outperforming many state-of-the-art models on CIFAR datasets.
In the era of bathing in big data, it is common to see enormous amounts of data generated daily. As for the medical industry, not only could we collect a large amount of data, but also see each data set with a great number of features. When the number of features is ramping up, a common dilemma is adding computational cost during inferring.
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