用ImageNet自监督特征实现无需分割图的角膜神经扭曲度分级
Self-Supervised ImageNet Representations for In Vivo Confocal Microscopy: Tortuosity Grading without Segmentation Maps
- 用ImageNet预训练的DINO模型迁移至活体共聚焦显微图像
- 准确率84.25%，敏感度77.97%，超越现有方法
- 不依赖分割图，专注关键形态特征，适合临床辅助诊断
角膜神经纤维的扭曲度是多种疾病的重要指标。当前最先进的分级方法严重依赖昂贵的神经纤维分割图。本文证明ImageNet自监督预训练特征可有效迁移至活体共聚焦显微成像领域。尽管DINO已被后续版本超越，但经仔细微调后，其在准确率（84.25%）和敏感度（77.97%）上优于现有最优方法。所提模型无需分割图，聚焦关键形态学特征进行分级。
原文摘要 · Abstract (English)
The tortuosity of corneal nerve fibers are used as indication for different diseases. Current state-of-the-art methods for grading the tortuosity heavily rely on expensive segmentation maps of these nerve fibers. In this paper, we demonstrate that self-supervised pretrained features from ImageNet are transferable to the domain of in vivo confocal microscopy. We show that DINO should not be disregarded as a deep learning model for medical imaging, although it was superseded by two later versions. After careful fine-tuning, DINO improves upon the state-of-the-art in terms of accuracy (84,25%) and sensitivity (77,97%). Our fine-tuned model focuses on the key morphological elements in grading without the use of segmentation maps.
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