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Naslov:A VAN-Based Multi-Scale Cross-Attention Mechanism for Skin Lesion Segmentation Network
Avtorji:ID Liu, Shuang (Avtor)
ID Zhuang, Zeng (Avtor)
ID Zheng, Yanfeng (Avtor)
ID Kolmanič, Simon (Avtor)
Datoteke:.pdf Liu-2023-A_VAN-Based_Multi-Scale_Cross-Attenti.pdf (1,46 MB)
MD5: 4C68ADFAEEFDEB907786E05A22505E80
 
URL https://ieeexplore.ieee.org/document/10194499
 
Jezik:Angleški jezik
Vrsta gradiva:Članek v reviji
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:FERI - Fakulteta za elektrotehniko, računalništvo in informatiko
Opis:With the rise of deep learning technology, the field of medical image segmentation has undergone rapid development. In recent years, convolutional neural networks (CNNs) have brought many achievements and become the consensus in medical image segmentation tasks. Although many neural networks based on U-shaped structures and methods, such as skip connections have achieved excellent results in medical image segmentation tasks, the properties of convolutional operations limit their ability to effectively learn local and global features. To address this problem, the Transformer from the field of natural language processing (NLP) was introduced to the image segmentation field. Various Transformer-based networks have shown significant performance advantages over mainstream neural networks in different visual tasks, demonstrating the huge potential of Transformers in the field of image segmentation. However, Transformers were originally designed for NLP and ignore the multidimensional nature of images. In the process of operation, they may destroy the 2D structure of the image and cannot effectively capture low-level features. Therefore, we propose a new multi-scale cross-attention method called M-VAN Unet, which is designed based on the Visual Attention Network (VAN) and can effectively learn local and global features. We propose two attention mechanisms, namely MSC-Attention and LKA-Cross-Attention, for capturing low-level features and promoting global information interaction. MSC-Attention is designed for multi-scale channel attention, while LKA-Cross-Attention is a cross-attention mechanism based on the large kernel attention (LKA). Extensive experiments show that our method outperforms current mainstream methods in evaluation metrics such as Dice coefficient and Hausdorff 95 coefficient.
Ključne besede:CNNs, deep learning, medical image processing, NLP, semantic segmentation
Status publikacije:Objavljeno
Verzija publikacije:Objavljena publikacija
Poslano v recenzijo:17.06.2023
Datum sprejetja članka:19.07.2023
Datum objave:25.07.2023
Založnik:Institute of Electrical and Electronics Engineers (IEEE)
Leto izida:2023
Št. strani:Str. 81953 - 81964
Številčenje:Letn. 11
PID:20.500.12556/DKUM-87384 Novo okno
UDK:004.9
COBISS.SI-ID:162859779 Novo okno
DOI:10.1109/ACCESS.2023.3298826 Novo okno
ISSN pri članku:2169-3536
Datum objave v DKUM:14.03.2024
Število ogledov:431
Število prenosov:305
Metapodatki:XML RDF-CHPDL DC-XML DC-RDF
Področja:Ostalo
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Vaša ocena:Ocenjevanje je dovoljeno samo prijavljenim uporabnikom.
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Gradivo je del revije

Naslov:IEEE access
Založnik:Institute of Electrical and Electronics Engineers
ISSN:2169-3536
COBISS.SI-ID:519839513 Novo okno

Gradivo je financirano iz projekta

Financer:Drugi - Drug financer ali več financerjev
Program financ.:Ministry of Science and Technology (financer)
Številka projekta:13-20
Naslov:Chinese–Slovenian Scientific and Technological 2021 Cooperation Project

Financer:Drugi - Drug financer ali več financerjev
Program financ.:Provincial Social Science Association (financer)
Številka projekta:2023lslybkt-039
Naslov:Liaoning Province Economic and Social Development Research Project 2023

Licence

Licenca:CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
Povezava:http://creativecommons.org/licenses/by/4.0/deed.sl
Opis:To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
Začetek licenciranja:25.07.2023

Sekundarni jezik

Jezik:Slovenski jezik
Ključne besede:globoko učenje, procesiranje medicinskih slik, semantična segmentacija


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