Augmentation Strategy for DINO in Glaucoma Classification

Annotated medical images remain scarce and costly to obtain, which motivates self-supervised learning (SSL). We present a controlled empirical study of two SSL frameworks, SimSiam and SimCLR, for retinal fundus classification, asking whether retina-specific augmentations (Graham's local-mean subtraction and CLAHE) improve representations beyond standard augmentation and grayscale conversion. ResNet-18 backbones are pretrained on 48K unlabeled fundus images and evaluated by linear probing and fine-tuning on EDID (multi-disease) and RetinaMNIST (DR grading). On EDID, grayscale conversion accounts for most of SimSiam's linear-probe gain, and Graham or CLAHE add little on top of it. After fine-tuning, SimSiam with Graham+Gray exceeds an ImageNet-supervised baseline on RetinaMNIST (+3.0 accuracy points) but only matches it on EDID. Retina-specific augmentation therefore helps inconsistently and should be validated per SSL method and task.

Despite the widespread adoption of foundation models as feature extractors for medical imaging, relatively little is understood about how different pretraining strategies influence the transferability of learned representations to weakly supervised ophthalmic imaging tasks. We investigate this question in ultra-widefield (UWF) retinal imaging by evaluating foundation model representations within a patch-based multiple instance learning (MIL) framework for disease classification on UWF images. We compare Vision Transformer encoders pretrained with supervised, Masked Autoencoder (MAE), and self-distillation objectives, while keeping the downstream aggregation architecture unchanged. Within a controlled comparison of ViT-B encoders pretrained on ImageNet-1k, the choice of pretraining objective substantially influenced frozen representation transfer, with supervised and self-distillation-based models outperforming MAE. A contemporary DINOv3 model pretrained at a larger scale achieved the strongest overall performance, with a quadratic weighted kappa of 0.863 for five-class diabetic retinopathy grading, comparable with DINOv1. Attention analysis further revealed distinct patch-aggregation behaviours associated with the different pretrained representations, while partial fine-tuning substantially reduced the performance gap for MAE. These findings suggest that pretraining strategy influences both representation transferability and the subsequent aggregation of patch-level evidence within MIL, resulting in differences in downstream classification performance.

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.