Efficient Multi-Granularity Knowledge Transfer for Radiology Report Generation
Radiology report generation can automatically generate clinical descriptions from X-ray images, thereby significantly improving the efficiency of radiologists.
ProofPaper ↗
Key points
- Accordingly, we herein propose a novel and compact Efficient Multi-Granularity Knowledge Transfer (EMGKT) method to address the above issues.
- First, we encode global knowledge embeddings using a medical vision-language model, which provides contextual medical knowledge.
- Moreover, we devise a novel Fine-Grained Knowledge Distillation (FGKD) training task which efficiently extract fine-grained knowledge.
- Finally, we further develop a mixture of disease diagnosis expert classifiers to enhance knowledge extraction.
Sources (1)
- [1]Efficient Multi-Granularity Knowledge Transfer for Radiology Report GenerationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 06:09 AM
Radiology report generation can automatically generate clinical descriptions from X-ray images, thereby significantly improving the efficiency of radiologists.
Accordingly, we herein propose a novel and compact Efficient Multi-Granularity Knowledge Transfer (EMGKT) method to address the above issues.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 8, 2026One Block, Multiple Depths: Recurrent Vision Transformers with Depth-Programmed Experts
- Oct 8, 2026SpatialOPSD: Self-Distilling Spatial Intelligence from Verified Coding Agent Traces
- Oct 8, 2026Distilling Routed 3D Privilege for Spatial Reasoning in Vision-Language Models
- Oct 7, 2026MIMESIS: Learning User Simulators as Training Environments for Interactive Agents
- Oct 7, 2026Composing What Each Teacher Learned: Multi-Teacher On-Policy Distillation through Teacher-Relative Shifts
- Oct 7, 2026On-Policy Distillation Teaches New Skills but Not New Knowledge