mlsanityData to 14 Oct 2026

LIMITR: Leveraging Local Information for Medical Image-Text Representation

arXiv 2303.11755 ↗First posted 21 Mar 2023cs.CVcited by 13

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Abstract (arXiv)

Medical imaging analysis plays a critical role in the diagnosis and treatment of various medical conditions. This paper focuses on chest X-ray images and their corresponding radiological reports. It presents a new model that learns a joint X-ray image & report representation. The model is based on a novel alignment scheme between the visual data and the text, which takes into account both local and global information. Furthermore, the model integrates domain-specific information of two types -- lateral images and the consistent visual structure of chest images. Our representation is shown to benefit three types of retrieval tasks: text-image retrieval, class-based retrieval, and phrase-grounding.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 20 of its 29 references that are in the map. First posted on arXiv 21 Mar 2023.

Builds on

20 of 29 references.

References · 20

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8 more
  1. 2020COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Imagescited by 264
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  8. 2017Learning Semantic Concepts and Order for Image and Sentence Matchingcited by 62

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Citing papers per year: 2023: 3; 2024: 8; 2025: 2.

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