Comparative performance analysis of our LKDA-Net with other existing methods on ACDC dataset. Best results are in bold. Note: RV: Right ventricle, Myo: Myocardium, LV: Left ventricle.

Comparative performance analysis of our LKDA-Net with other existing methods on ACDC dataset. Best results are in bold. Note: RV: Right ventricle, Myo: Myocardium, LV: Left ventricle.

<p>Comparative performance analysis of our LKDA-Net with other existing methods on ACDC dataset. Best results are in bold. Note: RV: Right ventricle, Myo: Myocardium, LV: Left ventricle.</p>

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Bibliographic Details
Main Author:	Ming Li (91180) (author)
Other Authors:	Jingang Ma (6007391) (author), Jing Zhao (21160) (author)
Published:	2025
Subjects:	Ecology Space Science Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified reduce channel redundancy hybrid architecture methods enhance feature expression dimensional medical images dimensional medical datasets depthwise convolutional augmentation demonstrated excellent performance volumetric segmentation ability dimensional volumetric segmentation dimensional convolutional network three public datasets large receptive fields div >< p swin transformer module swin transformer segmentation performance convolutional networks network adopts hierarchical transformer various architectures sized large recent works namely synapse model ’ lightweight three largely attributed large number investigate different inverted bottleneck experimental evaluations effectively utilize also introduced accurate three
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