R2Gen-Mamba: A Selective State Space Model for Radiology Report Generation
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arXiv
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| Format: | Preprint |
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2024
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| author | Sun, Yongheng Lee, Yueh Z. Woodard, Genevieve A. Zhu, Hongtu Lian, Chunfeng Liu, Mingxia |
| author_facet | Sun, Yongheng Lee, Yueh Z. Woodard, Genevieve A. Zhu, Hongtu Lian, Chunfeng Liu, Mingxia |
| contents | Radiology report generation is crucial in medical imaging,but the manual annotation process by physicians is time-consuming and labor-intensive, necessitating the develop-ment of automatic report generation methods. Existingresearch predominantly utilizes Transformers to generateradiology reports, which can be computationally intensive,limiting their use in real applications. In this work, we presentR2Gen-Mamba, a novel automatic radiology report genera-tion method that leverages the efficient sequence processingof the Mamba with the contextual benefits of Transformerarchitectures. Due to lower computational complexity ofMamba, R2Gen-Mamba not only enhances training and in-ference efficiency but also produces high-quality reports.Experimental results on two benchmark datasets with morethan 210,000 X-ray image-report pairs demonstrate the ef-fectiveness of R2Gen-Mamba regarding report quality andcomputational efficiency compared with several state-of-the-art methods. The source code can be accessed online. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_18135 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | R2Gen-Mamba: A Selective State Space Model for Radiology Report Generation Sun, Yongheng Lee, Yueh Z. Woodard, Genevieve A. Zhu, Hongtu Lian, Chunfeng Liu, Mingxia Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Radiology report generation is crucial in medical imaging,but the manual annotation process by physicians is time-consuming and labor-intensive, necessitating the develop-ment of automatic report generation methods. Existingresearch predominantly utilizes Transformers to generateradiology reports, which can be computationally intensive,limiting their use in real applications. In this work, we presentR2Gen-Mamba, a novel automatic radiology report genera-tion method that leverages the efficient sequence processingof the Mamba with the contextual benefits of Transformerarchitectures. Due to lower computational complexity ofMamba, R2Gen-Mamba not only enhances training and in-ference efficiency but also produces high-quality reports.Experimental results on two benchmark datasets with morethan 210,000 X-ray image-report pairs demonstrate the ef-fectiveness of R2Gen-Mamba regarding report quality andcomputational efficiency compared with several state-of-the-art methods. The source code can be accessed online. |
| title | R2Gen-Mamba: A Selective State Space Model for Radiology Report Generation |
| topic | Computation and Language Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2410.18135 |