Text-based Medical Image Retrieval using Convolutional Neural Network and Speciﬁc Medical Features

Souissi, Nada; Ayadi, Hajer; Torjmen-Khemakhem, Mouna

doi:10.5220/0007355400780087

Cited by 3 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%

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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%

“…In our previous work [ 13 ], we proposed a personalized CNN model that considers the specificity of images in its retrieval process. In that model, we consider the Word2Vec model for word embedding.…”

Section: Introductionmentioning

confidence: 99%