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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.12347 |
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| _version_ | 1866914952044871680 |
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| author | He, Weijie Bao, Runyuan Cang, Yiru Wei, Jianjun Zhang, Yang Hu, Jiacheng |
| author_facet | He, Weijie Bao, Runyuan Cang, Yiru Wei, Jianjun Zhang, Yang Hu, Jiacheng |
| contents | This paper delves into the challenges and advancements in the field of medical image segmentation, particularly focusing on breast cancer diagnosis. The authors propose a novel Transformer-based segmentation model that addresses the limitations of traditional convolutional neural networks (CNNs), such as U-Net, in accurately localizing and segmenting small lesions within breast cancer images. The model introduces an axial attention mechanism to enhance the computational efficiency and address the issue of global contextual information that is often overlooked by CNNs. Additionally, the paper discusses improvements tailored to the small dataset challenge, including the incorporation of relative position information and a gated axial attention mechanism to refine the model's focus on relevant features. The proposed model aims to significantly improve the segmentation accuracy of breast cancer images, offering a more efficient and effective tool for computer-aided diagnosis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_12347 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Axial Attention Transformer Networks: A New Frontier in Breast Cancer Detection He, Weijie Bao, Runyuan Cang, Yiru Wei, Jianjun Zhang, Yang Hu, Jiacheng Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning This paper delves into the challenges and advancements in the field of medical image segmentation, particularly focusing on breast cancer diagnosis. The authors propose a novel Transformer-based segmentation model that addresses the limitations of traditional convolutional neural networks (CNNs), such as U-Net, in accurately localizing and segmenting small lesions within breast cancer images. The model introduces an axial attention mechanism to enhance the computational efficiency and address the issue of global contextual information that is often overlooked by CNNs. Additionally, the paper discusses improvements tailored to the small dataset challenge, including the incorporation of relative position information and a gated axial attention mechanism to refine the model's focus on relevant features. The proposed model aims to significantly improve the segmentation accuracy of breast cancer images, offering a more efficient and effective tool for computer-aided diagnosis. |
| title | Axial Attention Transformer Networks: A New Frontier in Breast Cancer Detection |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2409.12347 |