A medical image retrieval scheme with relevance feedback through a medical social network

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Mouhamed Gaith Ayadi ◽  
Riadh Bouslimi ◽  
Jalel Akaichi
Keyword(s):  
Social Network ◽  
Image Retrieval ◽  
Relevance Feedback ◽  
Medical Image ◽  
Medical Image Retrieval ◽  
Medical Social Network ◽  
Retrieval Scheme

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 Based on Semi-Supervised Learning

2010 ◽  
Vol 108-111 ◽  
pp. 201-206 ◽  
Author(s):  
Hui Liu ◽  
Cai Ming Zhang ◽  
Hua Han
Keyword(s):  
Support Vector Machine ◽  
Active Learning ◽  
Image Retrieval ◽  
Supervised Learning ◽  
Relevance Feedback ◽  
Medical Image ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Learning Support ◽  
Medical Image Retrieval

Among various content-based image retrieval (CBIR) methods based on active learning, support vector machine(SVM) active learning is popular for its application to relevance feedback in CBIR. However, the regular SVM active learning has two main drawbacks when used for relevance feedback. Furthermore, it’s difficult to collect vast amounts of labeled data and easy for unlabeled data to image examples. Therefore, it is necessary to define conditions to utilize the unlabeled examples enough. This paper presented a method of medical images retrieval about semi-supervised learning based on SVM for relevance feedback in CBIR. This paper also introduced an algorithm about defining two learners, both learners are re-trained after every relevance feedback round, and then each of them gives every image in a rank. Experiments show that using semi-supervised learning idea in CBIR is beneficial, and the proposed method achieves better performance than some existing methods.

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