A Cluster-Based Approach for Semantic Similarity in the Biomedical Domain

Al-Mubaid, Hisham; Nguyen, Hoa A.

doi:10.1109/iembs.2006.4398006

Cited by 29 publications

(46 citation statements)

References 7 publications

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“…Let | path ( x i , x j )| ≥ 0 be the length of this path. In order to quantify a semantic dissimilarity value between x i and x j , an intuitive method has been originally proposed in (Al-Mubaid and Nguyen, 2006). It relies on a cluster-based strategy that combines the minimum path length between the semantic terms and the taxonomical depth of the considered branches.…”

Section: Methodsmentioning

confidence: 99%

On combining image-based and ontological semantic dissimilarities for medical image retrieval applications

Kurtz

Depeursinge

Napel

et al. 2014

Medical Image Analysis

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Computer-assisted image retrieval applications can assist radiologists by identifying similar images in archives as a means to providing decision support. In the classical case, images are described using low-level features extracted from their contents, and an appropriate distance is used to find the best matches in the feature space. However, using low-level image features to fully capture the visual appearance of diseases is challenging and the semantic gap between these features and the high-level visual concepts in radiology may impair the system performance. To deal with this issue, the use of semantic terms to provide high-level descriptions of radiological image contents has recently been advocated. Nevertheless, most of the existing semantic image retrieval strategies are limited by two factors: they require manual annotation of the images using semantic terms and they ignore the intrinsic visual and semantic relationships between these annotations during the comparison of the images. Based on these considerations, we propose an image retrieval framework based on semantic features that relies on two main strategies: (1) automatic “soft” prediction of ontological terms that describe the image contents from multi-scale Riesz wavelets and (2) retrieval of similar images by evaluating the similarity between their annotations using a new term dissimilarity measure, which takes into account both image-based and ontological term relations. The combination of these strategies provides a means of accurately retrieving similar images in databases based on image annotations and can be considered as a potential solution to the semantic gap problem. We validated this approach in the context of the retrieval of liver lesions from computed tomographic (CT) images and annotated with semantic terms of the RadLex ontology. The relevance of the retrieval results was assessed using two protocols: evaluation relative to a dissimilarity reference standard defined for pairs of images on a 25-images dataset, and evaluation relative to the diagnoses of the retrieved images on a 72-images dataset. A normalized discounted cumulative gain (NDCG) score of more than 0.92 was obtained with the first protocol, while AUC scores of more than 0.77 were obtained with the second protocol. This automatical approach could provide real-time decision support to radiologists by showing them similar images with associated diagnoses and, where available, responses to therapies.

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Section: Methodsmentioning

confidence: 99%

On combining image-based and ontological semantic dissimilarities for medical image retrieval applications

Kurtz

Depeursinge

Napel

et al. 2014

Medical Image Analysis

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“…To this end, we define α ( x i , x j ) as

α_{(x_{i}, x_{j})} = α_{(x_{j}, x_{i})} = {\begin{matrix} 0 & if x_{i} = x_{j} \\ S_{ϴ} (x_{i}, x_{j}) & otherwise \end{matrix}

where s Θ is an extension of the semantic dissimilarity measure proposed by Al-Mubaid and Nguyen in [37]. The original version of this measure and its proposed extension are presented hereinafter.…”

Section: Methodsmentioning

confidence: 99%

“…Consequently, most of the proposed measures act by counting the number of taxonomic links from each term to their Least Common Subsumer (LCS) ( i.e ., the most concrete taxonomical ancestor that subsumes these two terms) and also the number of links of the LCS to the root of the ontology. Among them, Al-Mubaid and Nguyen have proposed in [37] a measure based on a cluster-based strategy that combines both the minimum path length and the taxonomical depth of the considered branches. The definition of this measure was also extended to deal with terms belonging simultaneously to multiple ontologies [38], enabling to evaluate the term similarity from complementary sources of knowledge.…”

Section: Linguistic Proximity and Semantic Distancesmentioning

confidence: 99%

A hierarchical knowledge-based approach for retrieving similar medical images described with semantic annotations

Kurtz

Beaulieu

Napel

et al. 2014

Journal of Biomedical Informatics

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Computer-assisted image retrieval applications could assist radiologist interpretations by identifying similar images in large archives as a means to providing decision support. However, the semantic gap between low-level image features and their high level semantics may impair the system performances. Indeed, it can be challenging to comprehensively characterize the images using low-level imaging features to fully capture the visual appearance of diseases on images, and recently the use of semantic terms has been advocated to provide semantic descriptions of the visual contents of images. However, most of the existing image retrieval strategies do not consider the intrinsic properties of these terms during the comparison of the images beyond treating them as simple binary (presence/absence) features. We propose a new framework that includes semantic features in images and that enables retrieval of similar images in large databases based on their semantic relations. It is based on two main steps: (1) annotation of the images with semantic terms extracted from an ontology, and (2) evaluation of the similarity of image pairs by computing the similarity between the terms using the Hierarchical Semantic-Based Distance (HSBD) coupled to an ontological measure. The combination of these two steps provides a means of capturing the semantic correlations among the terms used to characterize the images that can be considered as a potential solution to deal with the semantic gap problem. We validate this approach in the context of the retrieval and the classification of 2D regions of interest (ROIs) extracted from computed tomographic (CT) images of the liver. Under this framework, retrieval accuracy of more than 0.96 was obtained on a 30-images dataset using the Normalized Discounted Cumulative Gain (NDCG) index that is a standard technique used to measure the effectiveness of information retrieval algorithms when a separate reference standard is available. Classification results of more than 95% were obtained on a 77-images dataset. For comparison purpose, the use of the Earth Mover's Distance (EMD), which is an alternative distance metric that considers all the existing relations among the terms, led to results retrieval accuracy of 0.95 and classification results of 93% with a higher computational cost. The results provided by the presented framework are competitive with the state-of-the-art and emphasize the usefulness of the proposed methodology for radiology image retrieval and classification.

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“…Some of the most common methods rely on edge counting, shortest path, and ontological depth [6,14,15], while others add the least common subsumer (LCS) to capture the granularity of a concept in the ontology [16,17]. More recent advances have incorporated into similarity computation the distance to the LCS, assigning weights to the different path types (ontological depth, distance from concepts to LCS) [18], as well as all of the super concepts between two terms as a way to account for multiple inheritances [19].…”

Section: Introductionmentioning

confidence: 99%

A hybrid knowledge-based and data-driven approach to identifying semantically similar concepts

Pivovarov

Elhadad

2012

Journal of Biomedical Informatics

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An open research question when leveraging ontological knowledge is when to treat different concepts separately from each other and when to aggregate them. For instance, concepts for the terms "paroxysmal cough" and "nocturnal cough" might be aggregated in a kidney disease study, but should be left separate in a pneumonia study. Determining whether two concepts are similar enough to be aggregated can help build better datasets for data mining purposes and avoid signal dilution. Quantifying the similarity among concepts is a difficult task, however, in part because such similarity is context-dependent. We propose a comprehensive method, which computes a similarity score for a concept pair by combining data-driven and ontology-driven knowledge. We demonstrate our method on concepts from SNOMED-CT and on a corpus of clinical notes of patients with chronic kidney disease. By combining information from usage patterns in clinical notes and from ontological structure, the method can prune out concepts that are simply related from those which are semantically similar. When evaluated against a list of concept pairs annotated for similarity, our method reaches an AUC (area under the curve) of 92%.

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A Cluster-Based Approach for Semantic Similarity in the Biomedical Domain

Cited by 29 publications

References 7 publications

On combining image-based and ontological semantic dissimilarities for medical image retrieval applications

On combining image-based and ontological semantic dissimilarities for medical image retrieval applications

A hierarchical knowledge-based approach for retrieving similar medical images described with semantic annotations

A hybrid knowledge-based and data-driven approach to identifying semantically similar concepts

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