MAPUNetR: A Hybrid Vision Transformer and U-Net Architecture for Efficient and Interpretable Medical Image Segmentation
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866913567832276992 |
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| author | Shah, Ovais Iqbal Rizvi, Danish Raza Mir, Aqib Nazir |
| author_facet | Shah, Ovais Iqbal Rizvi, Danish Raza Mir, Aqib Nazir |
| contents | Medical image segmentation is pivotal in healthcare, enhancing diagnostic accuracy, informing treatment strategies, and tracking disease progression. This process allows clinicians to extract critical information from visual data, enabling personalized patient care. However, developing neural networks for segmentation remains challenging, especially when preserving image resolution, which is essential in detecting subtle details that influence diagnoses. Moreover, the lack of transparency in these deep learning models has slowed their adoption in clinical practice. Efforts in model interpretability are increasingly focused on making these models' decision-making processes more transparent. In this paper, we introduce MAPUNetR, a novel architecture that synergizes the strengths of transformer models with the proven U-Net framework for medical image segmentation. Our model addresses the resolution preservation challenge and incorporates attention maps highlighting segmented regions, increasing accuracy and interpretability. Evaluated on the BraTS 2020 dataset, MAPUNetR achieved a dice score of 0.88 and a dice coefficient of 0.92 on the ISIC 2018 dataset. Our experiments show that the model maintains stable performance and potential as a powerful tool for medical image segmentation in clinical practice. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_22223 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MAPUNetR: A Hybrid Vision Transformer and U-Net Architecture for Efficient and Interpretable Medical Image Segmentation Shah, Ovais Iqbal Rizvi, Danish Raza Mir, Aqib Nazir Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Medical image segmentation is pivotal in healthcare, enhancing diagnostic accuracy, informing treatment strategies, and tracking disease progression. This process allows clinicians to extract critical information from visual data, enabling personalized patient care. However, developing neural networks for segmentation remains challenging, especially when preserving image resolution, which is essential in detecting subtle details that influence diagnoses. Moreover, the lack of transparency in these deep learning models has slowed their adoption in clinical practice. Efforts in model interpretability are increasingly focused on making these models' decision-making processes more transparent. In this paper, we introduce MAPUNetR, a novel architecture that synergizes the strengths of transformer models with the proven U-Net framework for medical image segmentation. Our model addresses the resolution preservation challenge and incorporates attention maps highlighting segmented regions, increasing accuracy and interpretability. Evaluated on the BraTS 2020 dataset, MAPUNetR achieved a dice score of 0.88 and a dice coefficient of 0.92 on the ISIC 2018 dataset. Our experiments show that the model maintains stable performance and potential as a powerful tool for medical image segmentation in clinical practice. |
| title | MAPUNetR: A Hybrid Vision Transformer and U-Net Architecture for Efficient and Interpretable Medical Image Segmentation |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.22223 |