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LLM encoder improves chest X-ray image-text retrieval

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a domain-adapted large language model encoder to improve image-text retrieval for chest X-rays. This approach addresses challenges posed by varied and abbreviated radiology report styles by training the encoder to produce robust text embeddings. When integrated into a dual-tower contrastive framework, the model enhances alignment between X-ray images and their corresponding reports, leading to improved retrieval accuracy and generalization across different datasets. AI

IMPACT Enhances multimodal learning for medical imaging, potentially improving diagnostic accuracy and efficiency in radiology.

RANK_REASON The cluster contains an academic paper detailing a new method for multimodal learning in a specific medical domain. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM encoder improves chest X-ray image-text retrieval

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The cluster contains an academic paper detailing a new method for multimodal learning in a specific medical domain. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Hanbin Ko, Gihun Cho, Inhyeok Baek, Donguk Kim, Joonbeom Koo, Changi Kim, Dongheon Lee, Chang Min Park ·

    Exploring the Capabilities of Large Language Model Encoders for Image-Text Retrieval in Chest X-rays

    arXiv:2509.15234v2 Announce Type: replace Abstract: Multimodal learning from paired medical images and clinical text is a central challenge in medical data-driven informatics, where effective cross-modal alignment is critical for scalable analysis and retrieval. In chest radiogra…

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