# Self-Supervised ImageNet Representations for In Vivo Confocal Microscopy: Tortuosity Grading without Segmentation Maps

Source: [Self-Supervised ImageNet Representations for In Vivo Confocal Microscopy: Tortuosity Grading without Segmentation Maps](<https://www.sota2.com/research/paper/69b8e0430636e5bf4afbedc9>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-07-03T05:00:11.862Z

Authors: Kim Ouan, No\\'emie Moreau, Katarzyna Bozek

Published: 2026-03-16T13:36:38.000Z

## Abstract

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 We show that DINO should not be disregarded as a deep learning model for medical imaging, although it was superseded by two later versions.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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2603.15269>)

[PDF](<https://arxiv.org/pdf/2603.15269.pdf>)