Enhancing Diagnostic Reliability of Foundation Model with Uncertainty Estimation in OCT Images
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914848056541184 |
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| author | Peng, Yuanyuan Lin, Aidi Wang, Meng Lin, Tian Zou, Ke Cheng, Yinglin Shi, Tingkun Liao, Xulong Feng, Lixia Liang, Zhen Chen, Xinjian Fu, Huazhu Chen, Haoyu |
| author_facet | Peng, Yuanyuan Lin, Aidi Wang, Meng Lin, Tian Zou, Ke Cheng, Yinglin Shi, Tingkun Liao, Xulong Feng, Lixia Liang, Zhen Chen, Xinjian Fu, Huazhu Chen, Haoyu |
| contents | Inability to express the confidence level and detect unseen classes has limited the clinical implementation of artificial intelligence in the real-world. We developed a foundation model with uncertainty estimation (FMUE) to detect 11 retinal conditions on optical coherence tomography (OCT). In the internal test set, FMUE achieved a higher F1 score of 96.76% than two state-of-the-art algorithms, RETFound and UIOS, and got further improvement with thresholding strategy to 98.44%. In the external test sets obtained from other OCT devices, FMUE achieved an accuracy of 88.75% and 92.73% before and after thresholding. Our model is superior to two ophthalmologists with a higher F1 score (95.17% vs. 61.93% &71.72%). Besides, our model correctly predicts high uncertainty scores for samples with ambiguous features, of non-target-category diseases, or with low-quality to prompt manual checks and prevent misdiagnosis. FMUE provides a trustworthy method for automatic retinal anomalies detection in the real-world clinical open set environment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_16942 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Diagnostic Reliability of Foundation Model with Uncertainty Estimation in OCT Images Peng, Yuanyuan Lin, Aidi Wang, Meng Lin, Tian Zou, Ke Cheng, Yinglin Shi, Tingkun Liao, Xulong Feng, Lixia Liang, Zhen Chen, Xinjian Fu, Huazhu Chen, Haoyu Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Inability to express the confidence level and detect unseen classes has limited the clinical implementation of artificial intelligence in the real-world. We developed a foundation model with uncertainty estimation (FMUE) to detect 11 retinal conditions on optical coherence tomography (OCT). In the internal test set, FMUE achieved a higher F1 score of 96.76% than two state-of-the-art algorithms, RETFound and UIOS, and got further improvement with thresholding strategy to 98.44%. In the external test sets obtained from other OCT devices, FMUE achieved an accuracy of 88.75% and 92.73% before and after thresholding. Our model is superior to two ophthalmologists with a higher F1 score (95.17% vs. 61.93% &71.72%). Besides, our model correctly predicts high uncertainty scores for samples with ambiguous features, of non-target-category diseases, or with low-quality to prompt manual checks and prevent misdiagnosis. FMUE provides a trustworthy method for automatic retinal anomalies detection in the real-world clinical open set environment. |
| title | Enhancing Diagnostic Reliability of Foundation Model with Uncertainty Estimation in OCT Images |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.16942 |