تقرير
Enhancing Diagnostic Reliability of Foundation Model with Uncertainty Estimation in OCT Images
| العنوان: | Enhancing Diagnostic Reliability of Foundation Model with Uncertainty Estimation in OCT Images |
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| المؤلفون: | 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 |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | 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. Comment: All codes are available at https://github.com/yuanyuanpeng0129/FMUE |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2406.16942 |
| رقم الأكسشن: | edsarx.2406.16942 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |