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<div><p>This paper introduces an optimized nested UNet model for automated left ventricular segmentation in cardiac function assessment. We utilize the EchoNet-Dynamic dataset, which contains both video data and expert annotations. Unlike conventional methods such as DeepLabv3 that strug...

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Bibliographic Details
Main Author:	Kerang Cao (21602045) (author)
Other Authors:	Miao Zhao (527763) (author), Minghui Geng (21602048) (author), Shuai Zheng (482786) (author), Hoekyung Jung (21602051) (author)
Published:	2025
Subjects:	Biotechnology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified unlike conventional methods reducing model size reduce computational overhead large model sizes experimental results reveal effectively capture multi combine binary cross cardiac function assessments cardiac function assessment address gradient issues proposed model introduces dice loss functions enhance feature selectivity simple attention module paper introduces dice coefficient attention module attention mechanism xlink "> video data stabilize training scale features optimized unet minimize redundancy lightweight simam imprecise segmentation expert annotations efficient solution dynamic dataset automated diagnosis also provide
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