Automatic Medical Image Multilingual Indexation Through a Medical Social Network

2017 ◽  
pp. 19-49
Author(s):  
Mouhamed Gaith Ayadi ◽  
Riadh Bouslimi ◽  
Jalel Akaichi ◽  
Hana Hedhli
Keyword(s):  
Social Network ◽  
Medical Image ◽  
Medical Social Network

Medical social network content analysis for medical image retrieval purpose

2019 ◽  
Vol 50 (1) ◽  
pp. 65-86
Author(s):  
Mouhamed Gaith Ayadi ◽  
Riadh Bouslimi ◽  
Jalel Akaichi
Keyword(s):  
Content Analysis ◽  
Social Network ◽  
Image Retrieval ◽  
Medical Image ◽  
Medical Image Retrieval ◽  
Medical Social Network

Semantic medical image retrieval in a medical social network

2016 ◽  
Vol 7 (1) ◽  
Author(s):  
Riadh Bouslimi ◽  
Mouhamed Gaith Ayadi ◽  
Jalel Akaichi
Keyword(s):  
Social Network ◽  
Image Retrieval ◽  
Medical Image ◽  
Medical Image Retrieval ◽  
Medical Social Network

Medical Image Retrieval in Healthcare Social Networks

2018 ◽  
Vol 13 (2) ◽  
pp. 13-28
Author(s):  
Riadh Bouslimi ◽  
Mouhamed Gaith Ayadi ◽  
Jalel Akaichi
Keyword(s):  
Social Network ◽  
Latent Semantic Analysis ◽  
Medical Image ◽  
Semantic Analysis ◽  
Ground Truth ◽  
Weight Vector ◽  
Research Model ◽  
Textual Description ◽  
Analysis Of Results ◽  
Single Modality

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.

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