Enhanced Image Representations for Medical Report Generation
Published:Dec 29, 2025 03:51
•1 min read
•ArXiv
Analysis
This paper addresses the challenge of generating medical reports from chest X-ray images, a crucial and time-consuming task. It highlights the limitations of existing methods in handling information asymmetry between image and metadata representations and the domain gap between general and medical images. The proposed EIR approach aims to improve accuracy by using cross-modal transformers for fusion and medical domain pre-trained models for image encoding. The work is significant because it tackles a real-world problem with potential to improve diagnostic efficiency and reduce errors in healthcare.
Key Takeaways
- •Addresses the information asymmetry problem between image and metadata representations.
- •Mitigates the domain gap between general and medical images.
- •Proposes a novel approach called Enhanced Image Representations (EIR).
- •Utilizes cross-modal transformers and medical domain pre-trained models.
- •Demonstrates effectiveness on MIMIC and Open-I datasets.
Reference
“The paper proposes a novel approach called Enhanced Image Representations (EIR) for generating accurate chest X-ray reports.”