DAC-Net: A light-weight U-shaped network based efficient convolution and attention for thyroid nodule segmentation

Recently, there has been a focused effort to improve the efficiency of thyroid nodule segmentation algorithms. This endeavor has resulted in the development of increasingly complex modules, such as the Transformer, leading to models with a higher number of parameters and computing requirements. Soph...

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Veröffentlicht in:	Computers in biology and medicine Jg. 180; S. 108972
Hauptverfasser:	Yang, Yingwei, Huang, Haiguang, Shao, Yingsheng, Chen, Beilei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Ltd 01.09.2024 Elsevier Limited
Schlagworte:	Algorithms Attention mechanism Clinical medicine Complexity Computation Convolution Deep learning Design parameters Feature extraction Feature maps Humans Image Interpretation, Computer-Assisted - methods Information processing Internal Medicine Light-weight model Modules Neural Networks, Computer Nodules Other Segmentation Source code Thyroid Thyroid gland Thyroid Nodule - diagnostic imaging Thyroid nodule segmentation U-shaped network Weight reduction Light-weight model Deep learning Attention mechanism Convolution Thyroid nodule segmentation U-shaped network
ISSN:	0010-4825, 1879-0534, 1879-0534
Online-Zugang:	Volltext
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