arXiv (OAI Expanded)
open access
Resolution scaling governs DINOv3 transfer performance in chest radiograph classification
Self-supervised learning (SSL) has improved visual representation learning, but its value in chest radiography remains uncertain. DINOv3 extends earlier SSL models through Gram-anchored self-distillation and explicit high-resolution adaptation. Whether these changes improve transfer learning for che
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- GeoTTER: Leveraging Local Geometry of Optimal Transport for Zero-Shot Classification#1071302
- Self-supervised and supervised learning in medical imaging classification: Addressing the hidden bias of workflow design#185371
- Resolution-dependent self-supervised transfer in chest radiograph classification.#685678
- BoundarySeg:Boundary-Aware Multi-Task Learning for Semi-Supervised 3D Medical Image Segmentation with Limited Unlabeled Data#972376
- Morphology-aware self-supervised learning for arrhythmia detection in electrocardiograms.#416241
Record · ID 1110125
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive