Semantic aware representation learning for optimizing image retrieval systems in radiology

IF 7.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Pattern Recognition Pub Date : 2024-10-01 DOI:10.1016/j.patcog.2024.111060

Zografoula Vagena , Xiaoyang Wei , Camille Kurtz , Florence Cloppet

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Abstract

Content-based image retrieval (CBIR), which consists of ranking a set of images with respect to a query image based on visual similarity, can assist diagnostic radiologists in assessing medical images, by identifying similar digital images in large image databases. Despite the many recent advances and innovations in CBIR for general images, their adoption in radiology has been slow and limited. In the current paper we attempt to close the gap between the two domains and wisely adapt modern CBIR techniques to radiology images: by extending the latest representation learning techniques in a way that can overcome the unique challenges and at the same time take advantage of the specific opportunities that are present in radiology we were able to come up with novel and effective medical image retrieval methods. Our method achieves the highest CUI@5 scores (18.48, 15.95) on two widely used datasets (ROCO and MEDICAT respectively), showcasing the superiority of the proposed method in comparison with state-of-the-art relevant alternatives.

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优化放射学图像检索系统的语义感知表征学习

基于内容的图像检索（CBIR）包括根据视觉相似性对一组图像与查询图像进行排序，通过识别大型图像数据库中的相似数字图像，可以帮助放射诊断医师评估医学图像。尽管最近在普通图像的 CBIR 方面取得了许多进展和创新，但它们在放射学中的应用却十分缓慢和有限。在本文中，我们试图缩小这两个领域之间的差距，并明智地将现代 CBIR 技术应用于放射学图像：通过扩展最新的表示学习技术，我们能够克服独特的挑战，同时利用放射学中存在的特殊机遇，提出新颖而有效的医学图像检索方法。我们的方法在两个广泛使用的数据集（分别为 ROCO 和 MEDICAT）上获得了最高的 CUI@5 分数（18.48 分和 15.95 分），与最先进的相关替代方法相比，展示了所提出方法的优越性。

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来源期刊

Pattern Recognition 工程技术-工程：电子与电气

CiteScore

14.40

自引率

16.20%

发文量

683

审稿时长

5.6 months

期刊介绍： The field of Pattern Recognition is both mature and rapidly evolving, playing a crucial role in various related fields such as computer vision, image processing, text analysis, and neural networks. It closely intersects with machine learning and is being applied in emerging areas like biometrics, bioinformatics, multimedia data analysis, and data science. The journal Pattern Recognition, established half a century ago during the early days of computer science, has since grown significantly in scope and influence.