A computationally frugal open-source foundation model for thoracic disease detection in lung cancer screening programs
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918093473710080 |
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| author | McConnell, Niccolò Vasudev, Pardeep Yamada, Daisuke Cheng, Daryl Azimbagirad, Mehran McCabe, John Aslani, Shahab Shahin, Ahmed H. Zhou, Yukun Consortium, The SUMMIT Altmann, Andre Hu, Yipeng Taylor, Paul Janes, Sam M. Alexander, Daniel C. Jacob, Joseph |
| author_facet | McConnell, Niccolò Vasudev, Pardeep Yamada, Daisuke Cheng, Daryl Azimbagirad, Mehran McCabe, John Aslani, Shahab Shahin, Ahmed H. Zhou, Yukun Consortium, The SUMMIT Altmann, Andre Hu, Yipeng Taylor, Paul Janes, Sam M. Alexander, Daniel C. Jacob, Joseph |
| contents | Low-dose computed tomography (LDCT) imaging employed in lung cancer screening (LCS) programs is increasing in uptake worldwide. LCS programs herald a generational opportunity to simultaneously detect cancer and non-cancer-related early-stage lung disease. Yet these efforts are hampered by a shortage of radiologists to interpret scans at scale. Here, we present TANGERINE, a computationally frugal, open-source vision foundation model for volumetric LDCT analysis. Designed for broad accessibility and rapid adaptation, TANGERINE can be fine-tuned off the shelf for a wide range of disease-specific tasks with limited computational resources and training data. Relative to models trained from scratch, TANGERINE demonstrates fast convergence during fine-tuning, thereby requiring significantly fewer GPU hours, and displays strong label efficiency, achieving comparable or superior performance with a fraction of fine-tuning data. Pretrained using self-supervised learning on over 98,000 thoracic LDCTs, including the UK's largest LCS initiative to date and 27 public datasets, TANGERINE achieves state-of-the-art performance across 14 disease classification tasks, including lung cancer and multiple respiratory diseases, while generalising robustly across diverse clinical centres. By extending a masked autoencoder framework to 3D imaging, TANGERINE offers a scalable solution for LDCT analysis, departing from recent closed, resource-intensive models by combining architectural simplicity, public availability, and modest computational requirements. Its accessible, open-source lightweight design lays the foundation for rapid integration into next-generation medical imaging tools that could transform LCS initiatives, allowing them to pivot from a singular focus on lung cancer detection to comprehensive respiratory disease management in high-risk populations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_01881 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A computationally frugal open-source foundation model for thoracic disease detection in lung cancer screening programs McConnell, Niccolò Vasudev, Pardeep Yamada, Daisuke Cheng, Daryl Azimbagirad, Mehran McCabe, John Aslani, Shahab Shahin, Ahmed H. Zhou, Yukun Consortium, The SUMMIT Altmann, Andre Hu, Yipeng Taylor, Paul Janes, Sam M. Alexander, Daniel C. Jacob, Joseph Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Low-dose computed tomography (LDCT) imaging employed in lung cancer screening (LCS) programs is increasing in uptake worldwide. LCS programs herald a generational opportunity to simultaneously detect cancer and non-cancer-related early-stage lung disease. Yet these efforts are hampered by a shortage of radiologists to interpret scans at scale. Here, we present TANGERINE, a computationally frugal, open-source vision foundation model for volumetric LDCT analysis. Designed for broad accessibility and rapid adaptation, TANGERINE can be fine-tuned off the shelf for a wide range of disease-specific tasks with limited computational resources and training data. Relative to models trained from scratch, TANGERINE demonstrates fast convergence during fine-tuning, thereby requiring significantly fewer GPU hours, and displays strong label efficiency, achieving comparable or superior performance with a fraction of fine-tuning data. Pretrained using self-supervised learning on over 98,000 thoracic LDCTs, including the UK's largest LCS initiative to date and 27 public datasets, TANGERINE achieves state-of-the-art performance across 14 disease classification tasks, including lung cancer and multiple respiratory diseases, while generalising robustly across diverse clinical centres. By extending a masked autoencoder framework to 3D imaging, TANGERINE offers a scalable solution for LDCT analysis, departing from recent closed, resource-intensive models by combining architectural simplicity, public availability, and modest computational requirements. Its accessible, open-source lightweight design lays the foundation for rapid integration into next-generation medical imaging tools that could transform LCS initiatives, allowing them to pivot from a singular focus on lung cancer detection to comprehensive respiratory disease management in high-risk populations. |
| title | A computationally frugal open-source foundation model for thoracic disease detection in lung cancer screening programs |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2507.01881 |