



@book {2199,
	title = {Supervised content based image retrieval using radiology reports},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {7325 LNCS},
	year = {2012},
	note = {Conference code: 91199Export Date: 21 November 2012Source: Scopusdoi: 10.1007/978-3-642-31298-4_30Language of Original Document: EnglishCorrespondence Address: Ramos, J.; INEB - Instituto de Engenharia Biom{\'e}dica, Faculdade de Engenharia, Universidade Do PortoPortugalReferences: Aisen, A.M., Broderick, L.S., Winer-Muram, H., Brodley, C.E., Kak, A.C., Pavlopoulou, C., Dy, J., Marchiori, A., Automated storage and retrieval of thin-section CT images to assist diagnosis: System description and preliminary assessment (2003) Radiology, 228 (1), pp. 265-270;Akgul, C.B., Rubin, D.L., Napel, S., Beaulieu, C.F., Greenspan, H., Acar, B., Content-Based image retrieval in radiology: Current status and future directions (2010) Journal of Digital Imaging, 24 (2), pp. 208-222; 
Dreyer, K.J., Kalra, M.K., Maher, M.M., Hurier, A.M., Asfaw, B.A., Schultz, T., Halpern, E.F., Thrall, J.H., Application of recently developed computer algorithm for automatic classification of unstructured radiology reports: Validation study1 (2005) Radiology, 234 (2), pp. 323-329; 
Hersh, W.R., M{\"u}lller, H., Jensen, J.R., Yang, J., Gorman, P.N., Ruch, P., Advancing biomedical image retrieval: Development and analysis of a test collection (2006) Journal of the American Medical Informatics Association: JAMIA, 13 (5), pp. 488-496; 
Kelly, D., Teevan, J., Implicit feedback for inferring user preference: A bibliography (2003) SIGIR Forum, 37 (2), pp. 18-28; 
Kockelkorn, T.T.J.P., De Jong, P.A., Gietema, H.A., Grutters, J.C., Prokop, M., Van Ginneken, B., Interactive annotation of textures in thoracic CT scans (2010) Proceedings of SPIE, 7624 (1), pp. 76240X-76240X-8. , March; 
Li, Q., Li, F., Shiraishi, J., Katsuragawa, S., Sone, S., Doi, K., Investigation of new psychophysical measures for evaluation of similar images on thoracic computed tomography for distinction between benign and malignant nodules (2003) Medical Physics, 30 (10), pp. 2584-2593; 
Van Rikxoort, E.M., De Hoop, B., Van De Vorst, S., Prokop, M., Van Ginneken, B., Automatic segmentation of pulmonary segments from volumetric chest CT scans (2009) IEEE Transactions on Medical Imaging, 28 (4), pp. 621-630; 
Sammon, J.W., A nonlinear mapping for data structure analysis (1969) IEEE Transactions on Computers, C-18 (5), pp. 401-409; 
Slaney, M., Weinberger, K., White, W., Learning a metric for music similarity (2008) Procedings of the 9th International Society for Music Information Retrieval Conference, pp. 313-318; 
Sluimer, I.C., Prokop, M., Hartmann, I., Van Ginneken, B., Automated classification of hyperlucency, fibrosis, ground glass, solid, and focal lesions in high-resolution CT of the lung (2006) Medical Physics, 33 (7), p. 2610; 
Yang, L., Jin, R., Mummert, L., Sukthankar, R., Goode, A., Zheng, B., Hoi, S.C., Satyanarayanan, M., A boosting framework for Visuality-Preserving distance metric learning and its application to medical image retrieval (2010) IEEE Transactions on Pattern Analysis and Machine Intelligence, 32 (1), pp. 30-44
},
	month = {2012///},
	pages = {249 - 258},
	address = {Aveiro},
	abstract = {Content based image retrieval (CBIR) is employed in medicine to improve radiologists{\textquoteright} diagnostic performance. Today effective medical CBIR systems are limited to specific applications, as to reduce the amount of medical knowledge to model. Although supervised approaches could ease the incorporation of medical expertise, its application is not common due to the scarce number of available user annotations. This paper introduces the application of radiology reports to supervise CBIR systems. The concept is to make use of the textual distances between reports to build a transformation in image space through a manifold learning algorithm. A comparison was made between the presented approach and non-supervised CBIR systems, using a Leave-One-Patient-Out evaluation in a database of computer tomography scans of interstitial lung diseases. Supervised CBIR augmented the mean average precision consistently with an increase between 3 to 8 points, which suggests supervision by radiology reports increases CBIR performance. {\textcopyright} 2012 Springer-Verlag.},
	keywords = {CBIR system, Computer tomography, Content based retrieval, Content-Based Image Retrieval, Diagnostic performance, Image analysis, Image space, Interstitial lung disease, Learning algorithms, Manifold learning algorithm, Medical computing, Medical knowledge, Radiation, Radiology, Radiology reports, User annotations},
	isbn = {03029743 (ISSN); 9783642312977 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84864135039\&partnerID=40\&md5=1aafa2e679e79b57baf3cdb50c5e4c97},
	author = {Ramos, J. and Kockelkorn, T. and Van Ginneken, B. and Viergever, M.A. and Ramos, R. and Campilho, A.}
}
