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arXiv Computer Vision By Xubin Zhong, Zheyu Zhang, Wenjian Qin, Ning Wen

Efficient Multi-Granularity Knowledge Transfer for Radiology Report Generation

Read the original on arXiv Computer Vision →

The paper introduces Efficient Multi-Granularity Knowledge Transfer (EMGKT) for radiology report generation, aiming to improve medical knowledge extraction from X‑ray images. EMGKT encodes global knowledge via a medical vision‑language model and employs Fine‑Grained Knowledge Distillation (FGKD) to learn fine‑level embeddings without extra priors during inference. Additionally, a mixture of disease‑diagnosis expert classifiers is used to further enhance knowledge extraction, and the method is shown to be effective and transferable across datasets and baselines.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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