Embedding-Based Representations for BRSET and mBRSET

BRSET and mBRSET are publicly available Brazilian ophthalmological datasets
composed of curated retinal fundus photographs with associated clinical and
demographic information. While these resources enable diverse research
applications, training deep learning models directly on high-resolution images
is computationally intensive and often restricted by privacy regulations
limiting the circulation of identifiable medical images. To address these
challenges and facilitate equitable reuse, this project provides a
comprehensive release of precomputed image embeddings for both datasets. These
representations were generated using state-of-the-art vision backbones: DINOv3
ViT-S/16 (384-d) and ViT-B/16 (768-d) for transformer-based features,
alongside ConvNeXt-Tiny (768-d) and ConvNeXt-Base (1024-d) for convolutional
features. All models were applied in inference-only mode with a standardized
preprocessing pipeline. Each fundus photograph was converted into a fixed-
length numerical vector and exported as a CSV file, where each row corresponds
to a single image and its respective embedding. These representations preserve
critical semantic and structural information, enabling downstream tasks such
as clustering, similarity search, multimodal modeling, disease classification,
and fairness assessment without requiring raw pixel access. By providing
scalable, privacy-preserving embeddings derived from Brazilian ophthalmic
data, this resource reduces computational barriers, accelerates AI model
development, and supports global research participation, particularly in low-
resource environments, ensuring that advanced ophthalmic AI tools are
accessible to a broader scientific community.