Attention Swin U-Net: Cross-Contextual Attention Mechanism for Skin Lesion Segmentation-Reference-Cited by-同舟云学术

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Attention Swin U-Net: Cross-Contextual Attention Mechanism for Skin Lesion Segmentation

Published:2023-04-18 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)
language:
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Author:

Aghdam Ehsan Khodapanah¹,Azad Reza²,Zarvani Maral³,Merhof Dorit⁴

Affiliation:

1. Shahid Beheshti University,Department of Electrical Engineering,Tehran,Iran

2. RWTH Aachen University,Institute of Imaging and Computer Vision,Aachen,Germany

3. Alzahra University,Department of Computer Engineering,Tehran,Iran

4. University of Regensburg,Faculty of Informatics and Data Science,Bayern,Germany

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10230311/10230322/10230337.pdf?arnumber=10230337

Reference19 articles.

1. An image is worth 16x16 words: Transformers for image recognition at scale;dosovitskiy,2020

2. Contextual attention network: Transformer meets u-net;reza;MICCAI International Workshop on Machine Learning in Medical Imaging,2022

3. Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC)

4. CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification

5. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

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4. A multi-level closing based segmentation framework for dermatoscopic images using ensemble deep network;International Journal of System Assurance Engineering and Management;2024-07-04

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