Modality-Projection Universal Model for Comprehensive Full-Body Medical Imaging Segmentation
Fuente:
arXiv
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| Autores principales: | , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913627328479232 |
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| author | Chen, Yixin Gao, Lin Gao, Yajuan Wang, Rui Lian, Jingge Meng, Xiangxi Duan, Yanhua Chai, Leiying Han, Hongbin Cheng, Zhaoping Xie, Zhaoheng |
| author_facet | Chen, Yixin Gao, Lin Gao, Yajuan Wang, Rui Lian, Jingge Meng, Xiangxi Duan, Yanhua Chai, Leiying Han, Hongbin Cheng, Zhaoping Xie, Zhaoheng |
| contents | The integration of deep learning in medical imaging has shown great promise for enhancing diagnostic, therapeutic, and research outcomes. However, applying universal models across multiple modalities remains challenging due to the inherent variability in data characteristics. This study aims to introduce and evaluate a Modality Projection Universal Model (MPUM). MPUM employs a novel modality-projection strategy, which allows the model to dynamically adjust its parameters to optimize performance across different imaging modalities. The MPUM demonstrated superior accuracy in identifying anatomical structures, enabling precise quantification for improved clinical decision-making. It also identifies metabolic associations within the brain-body axis, advancing research on brain-body physiological correlations. Furthermore, MPUM's unique controller-based convolution layer enables visualization of saliency maps across all network layers, significantly enhancing the model's interpretability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_19026 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Modality-Projection Universal Model for Comprehensive Full-Body Medical Imaging Segmentation Chen, Yixin Gao, Lin Gao, Yajuan Wang, Rui Lian, Jingge Meng, Xiangxi Duan, Yanhua Chai, Leiying Han, Hongbin Cheng, Zhaoping Xie, Zhaoheng Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition The integration of deep learning in medical imaging has shown great promise for enhancing diagnostic, therapeutic, and research outcomes. However, applying universal models across multiple modalities remains challenging due to the inherent variability in data characteristics. This study aims to introduce and evaluate a Modality Projection Universal Model (MPUM). MPUM employs a novel modality-projection strategy, which allows the model to dynamically adjust its parameters to optimize performance across different imaging modalities. The MPUM demonstrated superior accuracy in identifying anatomical structures, enabling precise quantification for improved clinical decision-making. It also identifies metabolic associations within the brain-body axis, advancing research on brain-body physiological correlations. Furthermore, MPUM's unique controller-based convolution layer enables visualization of saliency maps across all network layers, significantly enhancing the model's interpretability. |
| title | Modality-Projection Universal Model for Comprehensive Full-Body Medical Imaging Segmentation |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.19026 |