20.500.12556/DKUM-87384 A VAN-Based Multi-Scale Cross-Attention Mechanism for Skin Lesion Segmentation Network 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. CNNs deep learning medical image processing NLP semantic segmentation globoko učenje procesiranje medicinskih slik semantična segmentacija true false true Institute of Electrical and Electronics Engineers (IEEE) Angleški jezik Slovenski jezik https://ieeexplore.ieee.org/ Članek v reviji 2024-03-14 10:41:54 2024-03-14 11:06:54 2024-06-13 14:27:57 0000-00-00 00:00:00 2023 0 0 Str. 81953 - 81964 Letn. 11 25 July 2023 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2023-06-17 2023-07-19 2023-07-25 004.9 162859779 10.1109/ACCESS.2023.3298826 2169-3536 Liu-2023-A_VAN-Based_Multi-Scale_Cross-Attenti.pdf Liu-2023-A_VAN-Based_Multi-Scale_Cross-Attenti.pdf 1 4C68ADFAEEFDEB907786E05A22505E80 e5210a8ed38a20f05f9452c9e593fc8b111743642485ed0350e9d200cc3843b0 b8f93496-e1e9-11ee-8231-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=176876 https://ieeexplore.ieee.org/document/10194499 0 7744854f-e1ea-11ee-8231-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=176877 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0