Medical Image Retrieval in Healthcare Social Networks
| Autor: | Mouhamed Gaith Ayadi, Jalel Akaichi, Riadh Bouslimi |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Thesaurus (information retrieval) Ground truth Information Systems and Management Modality (human–computer interaction) Modalities Information retrieval Social network Latent semantic analysis Computer science business.industry Medicine (miscellaneous) 02 engineering and technology 03 medical and health sciences 030104 developmental biology 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Weight business Image retrieval Information Systems |
| Zdroj: | International Journal of Healthcare Information Systems and Informatics. 13:13-28 |
| ISSN: | 1555-340X 1555-3396 |
| DOI: | 10.4018/ijhisi.2018040102 |
| Popis: | In this article, the authors present a multimodal research model to research medical images based on multimedia information that is extracted from a radiological collaborative social network. The opinions shared on a medical image in a medico-social network is a textual description which in most cases requires cleaning by using a medical thesaurus. In addition, they describe the textual description and medical image in a TF-IDF weight vector using a “bag-of-words” approach. The authors then use latent semantic analysis to establish relationships between textual terms and visual terms in shared opinions on the medical image. The model is evaluated against the ImageCLEFmedbaseline, which is the ground truth for the experiments. The authors have conducted numerous experiments with different descriptors and many combinations of modalities. The analysis of results shows that when the model is based on two methods it can increase the performance of a research system based on a single modality both visually or textually. |
| Databáze: | OpenAIRE |
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