arXiv (All)
open access
DINO-Med: A Unified Patch-Based Adaptation Framework for Multi-Modal Medical Image Analysis Applied to Liver Fibrosis Staging
Adapting natural-image foundation models like DINOv3 to multi-modal medical imaging is challenging due to the significant domain gap between natural color images and multi-channel medical scans. We present a unified, patch-based framework that processes raw multimodal imaging through training-free r
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
- Radiomics Analysis of Focal Liver Lesions Based on Contrast-Enhanced Ultrasound Imaging#1044729
- Flemme: A Flexible and Modular Learning Platform for Medical Images#128305
- Vascular-aware mixture-of-experts with a texture-enhanced decoder for accurate X-ray vessel segmentation.#1167237
- BioDeformUNet: A Deep Learning Model for Biomechanically Informed Liver Image Registration#640144
- Ultrasound-Based Prediction of Cirrhosis Decompensation Using Large-Scale Computer Vision Models#927659
Record · ID 998913
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