{"title":"Semantic aware representation learning for optimizing image retrieval systems in radiology","authors":"","doi":"10.1016/j.patcog.2024.111060","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":49713,"journal":{"name":"Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":7.5000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pattern Recognition","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0031320324008112","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
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.
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.
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