Model comparison. A comparison of DA-UNet++ and other models in terms of parameter quantity and segmentation performance was made, where the abscissa represents the model’s parameter quantity, and the ordinate represents the model’s Dice similarity coefficient on the EchoNet-Dynamic dataset.

<p>Model comparison. A comparison of DA-UNet++ and other models in terms of parameter quantity and segmentation performance was made, where the abscissa represents the model’s parameter quantity, and the ordinate represents the model’s Dice similarity coefficient on the EchoNet-Dynamic dataset...

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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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Model comparison. A comparison of DA-UNet++ and other models in terms of parameter quantity and segmentation performance was made, where the abscissa represents the model’s parameter quantity, and the ordinate represents the model’s Dice similarity coefficient on the EchoNet-Dynamic dataset.

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