Mining correlations between medically dependent features and image retrieval models for query classification

Ayadi, Hajer; Torjmen-Khemakhem, Mouna; Daoud, Mariam; Huang, Jimmy Xiangji; Jemaa, Maher Ben

doi:10.1002/asi.23772

Cited by 6 publications

(1 citation statement)

References 27 publications

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“…Due to the positive impact of MDF on both retrieval performance [ 6 , 13 ] and query classification [ 32 , 33 ], we choose to integrate them into a deep matching model. In this study, we utilized the Unified Medical Language System (UMLS) as our semantic resource to construct a semantic similarity matrix, which represents the relationships between pairs of Medical Dependent Features (MDF).…”

Section: Overview Of Our Approachmentioning

confidence: 99%

Enhancing Medical Image Retrieval with UMLS-Integrated CNN-Based Text Indexing

Gasmi,

Ayadi,

Torjmen

2024

Diagnostics

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In recent years, Convolutional Neural Network (CNN) models have demonstrated notable advancements in various domains such as image classification and Natural Language Processing (NLP). Despite their success in image classification tasks, their potential impact on medical image retrieval, particularly in text-based medical image retrieval (TBMIR) tasks, has not yet been fully realized. This could be attributed to the complexity of the ranking process, as there is ambiguity in treating TBMIR as an image retrieval task rather than a traditional information retrieval or NLP task. To address this gap, our paper proposes a novel approach to re-ranking medical images using a Deep Matching Model (DMM) and Medical-Dependent Features (MDF). These features incorporate categorical attributes such as medical terminologies and imaging modalities. Specifically, our DMM aims to generate effective representations for query and image metadata using a personalized CNN, facilitating matching between these representations. By using MDF, a semantic similarity matrix based on Unified Medical Language System (UMLS) meta-thesaurus, and a set of personalized filters taking into account some ranking features, our deep matching model can effectively consider the TBMIR task as an image retrieval task, as previously mentioned. To evaluate our approach, we performed experiments on the medical ImageCLEF datasets from 2009 to 2012. The experimental results show that the proposed model significantly enhances image retrieval performance compared to the baseline and state-of-the-art approaches.

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Section: Overview Of Our Approachmentioning

confidence: 99%

Enhancing Medical Image Retrieval with UMLS-Integrated CNN-Based Text Indexing

Gasmi,

Ayadi,

Torjmen

2024

Diagnostics

Self Cite

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MF‐Re‐Rank: A modality feature‐based Re‐Ranking model for medical image retrieval

Ayadi

Torjmen-Khemakhem

Daoud

et al. 2018

Asso for Info Science & Tech

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One of the main challenges in medical image retrieval is the increasing volume of image data, which render it difficult for domain experts to find relevant information from large data sets. Effective and efficient medical image retrieval systems are required to better manage medical image information. Text‐based image retrieval (TBIR) was very successful in retrieving images with textual descriptions. Several TBIR approaches rely on models based on bag‐of‐words approaches, in which the image retrieval problem turns into one of standard text‐based information retrieval; where the meanings and values of specific medical entities in the text and metadata are ignored in the image representation and retrieval process. However, we believe that TBIR should extract specific medical entities and terms and then exploit these elements to achieve better image retrieval results. Therefore, we propose a novel reranking method based on medical‐image‐dependent features. These features are manually selected by a medical expert from imaging modalities and medical terminology. First, we represent queries and images using only medical‐image‐dependent features such as image modality and image scale. Second, we exploit the defined features in a new reranking method for medical image retrieval. Our motivation is the large influence of image modality in medical image retrieval and its impact on image‐relevance scores. To evaluate our approach, we performed a series of experiments on the medical ImageCLEF data sets from 2009 to 2013. The BM25 model, a language model, and an image‐relevance feedback model are used as baselines to evaluate our approach. The experimental results show that compared to the BM25 model, the proposed model significantly enhances image retrieval performance. We also compared our approach with other state‐of‐the‐art approaches and show that our approach performs comparably to those of the top three runs in the official ImageCLEF competition.

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