Med-RADIO: Reducing All Medical Domains Into One via Multi-Teacher Distillation

arXiv:2609.37682 · cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.CV

Submitted: 2026-09-29

Updated: 2026-09-29

Code: https://github.com/CAIR-HKISI/Med-RADIO

Terminology

• Distillation — 50× in this paper · explained in 2 episodes
• Foundation Model — 38× in this paper · explained in 3 episodes
• AUC — 18× in this paper · explained in 2 episodes
• Linear Probing — 18× in this paper · explained in 2 episodes
• Benchmark — 15× in this paper · explained in 2 episodes
• Foundation Models — 12× in this paper · explained in 7 episodes
• Knowledge Distillation — 9× in this paper · explained in 11 episodes
• Fine-Tuning — 7× in this paper · explained in 5 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Alignment — 6× in this paper · explained in 1 episode
• Adaptation — 3× in this paper · explained in 1 episode
• Regularization — 3× in this paper · explained in 3 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Downstream Tasks — 2× in this paper · explained in 2 episodes
• Ensemble Learning — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Adaptive Mechanism — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Dynamic Weighting — 1× in this paper · explained in 3 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Model Merging — 1× in this paper · explained in 2 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Online Adaptation — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes

Sources

• SAM 3: Segment Anything with Concepts
• Curia: A Multi-Modal Foundation Model for Radiology
• MRI-CORE: A Foundation Model for Magnetic Resonance Imaging
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Generalist Foundation Models from a Multimodal Dataset for 3D Computed Tomography
• Hulu-Med: A Transparent Generalist Model towards Holistic Medical Vision-Language Understanding
• UniMed-CLIP: Towards a Unified Image-Text Pretraining Paradigm for Diverse Medical Imaging Modalities
• Ensemble learning of pathology foundation models for precision oncology
• DINOv2: Learning Robust Visual Features without Supervision
• Vision Foundation Models for Computed Tomography
• Efficient Knowledge Distillation of SAM for Medical Image Segmentation
• VisionFM: a Multi-Modal Multi-Task Vision Foundation Model for Generalist Ophthalmic Artificial Intelligence
• Foundation Models in Medical Image Analysis: A Systematic Review and Meta-Analysis
• C-RADIOv4 (Tech Report)
• SAM 2: Segment Anything in Images and Videos
• From Task-Specific Models to Unified Systems: A Review of Model Merging Approaches
• EyeFound: A Multimodal Generalist Foundation Model for Ophthalmic Imaging
• EyeCLIP: A visual-language foundation model for multi-modal ophthalmic image analysis
• DINOv3
• Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation

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