DINOHist: Fine-Tuning Self-Supervised Foundation Models for Histopathological Image Retrieval - Figuerêdo, José Solenir L.; Calumby, Rodrigo Tripodi (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2026: Anais do XLI Simpósio Brasileiro de Bancos de Dados, 2026)
This study investigates self-supervised learning (SSL) for feature extraction in Content-Based Histopathological Image Retrieval (CBHIR). We evaluate pre-trained, domain-specific, and fine-tuned DINOv2 models (DINOHist-S and DINOHist-B) on glomerular datasets. The analysis includes comparisons with state-of-the-art histopathology-specific models to assess domain adaptation. Results demonstrate that fine-tuning enables DINOv2 to outperform state-of-the-art specialized models, even with limited labeled data. The proposed models achieve superior effectiveness, particularly at lower-ranking positions, with statistical significance. These findings show that fine-tuned foundation models can surpass specialized approaches while reducing computational costs.