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Automatic medical image multilingual annotation via a medical social network

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Abstract

Medical social networking sites enabled multimedia content sharing in large volumes, by allowing physicians and patients to upload their medical images. These images are diagnosed and commented, in different languages, by several specialists instantly. Moreover, it is necessary to employ new techniques, in order to automatically extract information and analyze knowledge from the huge number of comments expressing specialist’s analyzes and recommendations. For this reason, we propose a terms-based method in order to extract the relevant terms and words which can describe the medical image. Furthermore, significant extracted terms and keywords will be used later to index medical images, in order to facilitate their search through the social network site. In fact, we need to take account that existing comments are expressed in different languages. So, it is essential to implement a multilingual indexation method to eliminate the ambiguity which will be the cause of the effectiveness’s reduction of the search function. In order to palliate this situation, we propose a multilingual mixed approach which concentrates on algorithms based on statistical methods and external multilingual semantic resources, in order to handle and to cover different languages. The use of external resources, such as semantic multilingual thesaurus, can improve the efficiency of the indexing process. The proposed method can be applied in different languages. Our study is validated by a set of experiments and a comparison study with some existing approaches in literature. Experimental results have indicated that the proposed system has a superior performance compared to other systems. Finally, we need also to verify whether the system is considered by the specialists as a potential aid. We evaluate its impact, by inquiring the specialists about the degree of confidence in our system. By analyzing the obtained results, we can argue that the proposed system presented a high acceptance and viability rate regarding the specialists’ interests in the annotation practice domain.

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Notes

  1. http://iom.nationalacademies.org/

  2. http://corp.yougov.com/healthcare/consumers-use-preference-expectations-hospital-social-media

  3. http://www.sobercircle.com

  4. http://www.sparkpeople.com/

  5. https://www.fitocracy.com/

  6. https://www.dacadoo.com/

  7. http://www.asklepios.com/

  8. http://www.acc.org/

  9. http://www.diabspace.com/

  10. http://www.parlonscancer.ca/

  11. http://www.renaloo.com/

  12. http://www.rxspace.com/

  13. https://metamap.nlm.nih.gov/

  14. http://ai.stanford.edu/~rion/parsing/minipar_viz.html

  15. http://www.cis.uni-muenchen.de/~schmid/tools/TreeTagger/

  16. http://members.unine.ch/jacques.savoy/clef/index.html

  17. http://www.nzdl.org/Kea/download.html

  18. http://perso.univ-lyon2.fr/~maniezf/Corpus/Corpus_medical_FR_CRTT.htm

  19. http://www.fsjegj.rnu.tn/

  20. http://www.isg.rnu.tn/

References

  • Almansoori W, Zarour O, Jarada TN, Karampales P, Rokne J, Alhajj R (2011) Applications of social network construction and analysis in the medical referral process. In: Proceedings of the 2011 IEEE 9th International Conference on dependable, autonomic and secure computing (DASC‘11)

  • Barnard K, Forsyth D (2007) Learning the semantics of words and pictures. In: Proceedings of International Conference on Computer Vision

  • Bouslimi R, Akaichi J (2015) Automatic medical image annotation on social network of physician collaboration. Netw Model Anal Health Inform Bioinforma 4:10. doi:10.1007/s13721-015-0082-5

    Article  Google Scholar 

  • Bouslimi R, Messaoudi A, Akaichi J (2013) Using a bag of words for automatic medical image annotation with a latent semantic. Int J Artif Intell Appl 4(3)

  • Collins B, Deng J, Li K, Fei-Fei L (2008) Towards scalable dataset construction: an active learning approach. In: Proceedings of European Conference on Computer Vision

  • Daniel RG, Liza SR, Jennifer LK (2013) Dangers and opportunities for social media in medicine. Clin Obstet Gynecol. doi:10.1097/GRF.0b013e318297dc38

    Google Scholar 

  • Doganay S (2014) Healthcare social networks: new choices for doctors, patients. Available from: http://www.informationweek.com/healthcare/patient-tools/healthcare-social-networks-new-choices-for-doctors-patients/d/d-id/1234884

  • Feldman DL (2012) Medical social media networks: communicating across the virtual highway. Q J Health Care Pract Risk Manag Infocus 18(1):2–5

    Google Scholar 

  • Frakes WB, Fox CJ (2003) Strength and similarity of affix removal stemming algorithms. In: Newsletter of ACM SIGIR Forum Homepage archive, vol 37 Issue 1. New York, pp 26–30

  • Franklin V, Greene S (2007) Sweet talk: a text messaging support system. J Diabetes Nurs 11(1):22–26

    Google Scholar 

  • Fuming S, Yong G, Dongxia W, Xueming W (2010) A collaborative approach for image annotation. In: PSIVT, 2010, image and video technology, Pacific-Rim symposium on, image and video technology, Pacific-Rim symposium on 2010, pp 192–196. doi:10.1109/PSIVT.2010.39

  • Gaussier E, Maissonnasse L, Chevallet J–P (2008) Multiplying concept sources for graph modeling. In: CLEF 2007. LNCS 5152 proceedings, pp 585–592

  • Gong J, Sun S (2011) Individual doctor recommendation model on medical social network. In: Proceedings of the 7th International Conference on Advanced Data Mining and Applications (ADMA’11)

  • Grenier C (2003) The role of intermediate subject to understand the structuring of an organizational network of actors and technology—case of a care network. In: Proceedings of the 9th conference of the association information and management, Grenoble

  • Harrathi F (2010) Extraction de concepts et de relations entre concepts à partir des documents multilingues: approche statistique et ontologique. PhD Thesis, INSA Lyon

  • Jeon J, Lavrenko V, Manmatha R (2007) Automatic image annotation and retrieval using cross-media relevance models. In Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval

  • Jones K (1972) A statistical interpretation of term specificity and its application in retrieval. J Doc 28(1):11–21

    Article  Google Scholar 

  • Kanishcheva O and Angelova G (2015) A Pipeline Approach to Image Auto-Tagging Refinement. In: BCI ‘15 Proceedings of the 7th Balkan Conference on Informatics Conference, New York, NY, USA. doi:10.1145/2801081.2801108

  • Lacoste C, Chevallet J–P, Lim J-H, Wei X, Raccoceanu D, Hoang D, Vuillenemot F (2006) Ipal knowledge-based medical image retrieval in imageCLEFmed 2006. In: Working Notes for the CLEF 2006 Workshop, Alicante, Spain

  • Li J (2014) Data protection in healthcare social networks. J IEEE Softw 31(1):46–53

    Article  Google Scholar 

  • Li L-J, Fei-Fei L (2008) Optimol: automatic online picture collection via incremental model learning. In: International Journal of Computer Vision

  • Li Q, Lu SCY (2008) Collaborative tagging applications and approaches. IEEE Multimed 15(3):14–21

    Article  Google Scholar 

  • Maisonnasse L, Gaussier E, Chevallet J-P (2009) Combination of semantic analysis to search for medical information. In RISE (Research Information semantics) within the INFORSID’ conference, Toulouse

  • Makadia A, Pavlovic V, Kumar S (2008) A new baseline for image annotation. In: Proceedings of European Conference on Computer Vision

  • Manning CD, Raghavan P, Schütze H (2008) Introduction to information retrieval. Cambridge University Press, New York

    Book  MATH  Google Scholar 

  • Messaoudi A, Bouslimi R, Akaichi J (2013) Indexing medical images based on collaborative experts reports. Int J Comput Appl (0975-887) 70(5):1–9

    Google Scholar 

  • Mihalcea R, Leong C-W (2009) Towards communicating simple sentences using pictorial representations. Mach Transl 22:153–173

    Article  Google Scholar 

  • Neil S, Velte T, Jie H, Wei Z, Clement Y (2007) Knowledge intensive conceptual retrieval and passage extraction of biomedical literature. In: 30th annual 66 international ACM SIGIR conference on Research and development in information retrieval

  • NLM (2009) NLM Unified Medical Language System Fact Sheet. Avaible from: http://www.nlm.nih.gov/pubs/factsheets/umls.html (consulté le 23/04/2009)

  • Paukkeri M, Honkela T (2010) Likey: Unsupervised languageindependent keyphrase extraction. In: Proceedings of the 5th international workshop on semantic evaluation, Uppsala, Sweden, pp 162–165

  • Shevade B, Sundaram H, Xie L (2007) Modeling personal and social network context for event annotation in images. In: Proceedings of the 7th ACM/IEEE-CS joint conference on digital libraries, ACM, Vancouver, BC, Canada, pp 127–134

  • Stone Z, Zickler T, Darrell T (2008) Autotagging facebook: Social network context improves photo annotation. In: Proceedings of the 1st IEEE workshop on internet vision (CVPR 2008), p 8

  • Sun F, Ge Y, Wang D, Wang X (2010) A collaborative approach for image annotation. In: Proceedings of Fourth Pacific-Rim Symposium on Image and Video Technology (PSIVT). Singapore, pp 192–196. doi:10.1109/PSIVT.2010.39

  • Truran M, Goulding J, Ashman H (2005) Co-active intelligence for image retrieval. In Proceedings of the 13th annual ACM international conference on Multimedia, ACM, Hilton, Singapore, pp 547–550

  • Von Ahn L, Dabbish L (2004) Labeling images with a computer game. In: Proceedings of the SIGCHI conference on human factors in computing systems, ACM, Vienna, pp 319–326

  • Wang C, Blei D, Fei-Fei L (2009) Simultaneous image classification and annotation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition

  • Xie Y, Chen Z, Cheng Y, Zhang K, Agrawal A, Liao WK, Choudhary A (2013) Detecting and tracking disease outbreaks by mining social media data. In: Proceedings of the twenty-third international joint conference on artificial intelligence (IJCAI’13)

  • Zhi W, Wenwu Z, Peng C, Lifeng S, Shiqiang Y (2013) Social Media Recommendation. Soc Media Retr Comput Commun Netw. doi:10.1007/978-1-4471-4555-43

    Google Scholar 

  • Zunjarwad A, Sundaram H, Xie L (2007) Contextual wisdom: social relations and correlations for multimedia event annotation. In: Proceedings of the 15th international conference on Multimedia, ACM, Augsburg, pp 615–624

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Authors and Affiliations

  1. Department of Computer Sciences, ISG, BESTMOD, Tunis, Tunisia

    Mouhamed Gaith Ayadi, Riadh Bouslimi & Jalel Akaichi

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  1. Mouhamed Gaith Ayadi
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  2. Riadh Bouslimi
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  3. Jalel Akaichi
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Correspondence to Mouhamed Gaith Ayadi.

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Ayadi, M.G., Bouslimi, R. & Akaichi, J. Automatic medical image multilingual annotation via a medical social network. Netw Model Anal Health Inform Bioinforma 5, 20 (2016). https://doi.org/10.1007/s13721-016-0126-5

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  • DOI: https://doi.org/10.1007/s13721-016-0126-5

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