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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.12155 |
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| _version_ | 1866912587973656576 |
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| author | Bolhassani, M. Veasey, B. Daugherty, E. Keltner, S. Kumar, N. Dunlap, N. Amini, A. |
| author_facet | Bolhassani, M. Veasey, B. Daugherty, E. Keltner, S. Kumar, N. Dunlap, N. Amini, A. |
| contents | This study investigates the efficacy of Low-Rank Adaptation (LoRA) for fine-tuning large Vision Models, DinoV2 and SwinV2, to diagnose Radiation-Induced Lung Injury (RILI) from X-ray CT scans following Stereotactic Body Radiation Therapy (SBRT). To evaluate the robustness and efficiency of this approach, we compare LoRA with traditional full fine-tuning and inference-only (no fine-tuning) methods. Cropped images of two sizes (50 mm3 and 75 mm3), centered at the treatment isocenter, in addition to different adaptation techniques for adapting the 2D LVMs for 3D data were used to determine the sensitivity of the models to spatial context. Experimental results show that LoRA achieves comparable or superior performance to traditional fine-tuning while significantly reducing computational costs and training times by requiring fewer trainable parameters. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12155 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LoRA-fine-tuned Large Vision Models for Automated Assessment of Post-SBRT Lung Injury Bolhassani, M. Veasey, B. Daugherty, E. Keltner, S. Kumar, N. Dunlap, N. Amini, A. Computer Vision and Pattern Recognition This study investigates the efficacy of Low-Rank Adaptation (LoRA) for fine-tuning large Vision Models, DinoV2 and SwinV2, to diagnose Radiation-Induced Lung Injury (RILI) from X-ray CT scans following Stereotactic Body Radiation Therapy (SBRT). To evaluate the robustness and efficiency of this approach, we compare LoRA with traditional full fine-tuning and inference-only (no fine-tuning) methods. Cropped images of two sizes (50 mm3 and 75 mm3), centered at the treatment isocenter, in addition to different adaptation techniques for adapting the 2D LVMs for 3D data were used to determine the sensitivity of the models to spatial context. Experimental results show that LoRA achieves comparable or superior performance to traditional fine-tuning while significantly reducing computational costs and training times by requiring fewer trainable parameters. |
| title | LoRA-fine-tuned Large Vision Models for Automated Assessment of Post-SBRT Lung Injury |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.12155 |