MCBR-CDS 2012: Medical Content-based Retrieval for Clinical Decision Support; October 1st, 2012
The goal of this workshop is to bring together researchers in medical imaging, medical image retrieval, data mining, text retrieval, and in the machine learning/AI communities to discuss new techniques of multimodal mining/retrieval and their use in clinical decision support. We are looking for original, high-quality submissions that address innovative research and development in the analysis, search and retrieval of multimodal medical data. Further, to encourage a larger group of image analysis researchers to profit from the databases and evaluations created in the context of ImageCLEF, groups can get access to ImageCLEF 2012 images of the biomedical literature when registering.
SPRINGER PUBLICATION: All accepted papers will be published in a volume of the Springer Lecture Notes in Computer Science (LNCS).
SELECTED BEST PRIZE PAPER:
"Skull Retrieval for Craniosynostosis Using Sparse Logistic Regression Models"
(Shulin Yang, Linda Shapiro, Michael Cunningham, Matthew Speltz, Craig Birgfeld, Indriyati Atmosukarto, Su-In Lee)
Prize amount of 500 Euros is sponsored by The Khresmoi (http://khresmoi.eu/) project on medical information retrieval.
Camera ready copy : OCTOBER 15th, 2012
08:45 - 09:00 Opening
09:00 - 09:50 Invited Lecture 1: Gwenole Quellec
Laboratory of Medical Information Processing
Heterogeneous information retrieval from medical databases
09:50 - 10:30 3D methods
University hospital of Morvan
Brest CEDEX - FRANCE
Exploiting 3D part-based analysis, description and indexing
to support medical applications
Chiara Eva Catalano, Francesco Robbiano, Patrizia Parascandolo,
Lorenzo Cesario, Loris Vosilla, Francesca Barbieri, Michela Spagnuolo, Gianni Viano, Marco Amedeo Cimmino
Skull Retrieval for Craniosynostosis Using Sparse Logistic
Shulin Yang, Linda Shapiro, Michael Cunningham, Matthew Speltz,
Craig Birgfeld, Indriyati Atmosukarto, Su-In Lee
10:30 - 11:00 Coffee break
11:00 - 12:30 3D/4D data retrieval
Retrieval of 4D Dual Energy CT for Pulmonary Embolism
Antonio Foncubierta-Rodriguez, Alejandro Vargas, Alexandra
Platon, Pierre-Alexandre Poletti, Henning Muller, Adrien Depeursinge
Immediate ROI Search for 3-D Medical Images
Karen Simonyan, Marc Modat, Sebastien Ourselin, Antonio
Criminisi, Andrew Zisserman
Synergy of 3D SIFT and Sparse Codes for Classification of
viewpoints from Echocardiogram Videos
Yu Qian, Lianyi Wang, Chunyan Wang, Xiaohong Gao
Assessing the Classification of Liver Focal Lesions by Using
Multi-phase Computer Tomography Scans
Aureline Quatrehomme, Ingrid Millet, Denis Hoa, Gˇrard Subsol,
12:30 - 14:00 Lunch
14:00 - 14:50 Invited Lecture II : Georg Langs
Computational Image Analysis and Radiology Lab
Department of Radiology
Medical University of Vienna
Title: VISCERAL: Towards Large Data in Medical Imaging - Challenges and Directions
14:50 - 15:30 Visual features
Customised Frequency Pre-Filtering in a Local Binary Pattern-
Based Classification of Gastrointestinal Images
Georg Wimmer, Andreas Uhl
Bag of Colors for Biomedical Document Image
Alba Garc’a Seco de Herrera, Dimitrios Markonis, Henning Muller
15:30 - 16:00 Coffee break
16:00 - 16:30 Multimodal retrieval
An SVD-bypass Latent Semantic Analysis for Image
Spyridon Stathopoulos, Theodore Kalamboukis
Multimedia Retrieval in a Medical Image Collection: Results
Using Modality Classes (short paper)
Angel Castellanos, Ana Garc’a-Serrano, Xaro Benavent, Joan
16:30 - 17:30
PANEL + Open Discussion: What is the CBIR role in Medical Decision Support?
Call for papers
Diagnostic decision making (using images and other clinical data) is still very much an art for many physicians in their practices today due to a lack of quantitative tools and measurements. Traditionally, decision making has involved using evidence provided by the patient's data coupled with a physician's a priori experience of a limited number of similar cases. With advances in electronic patient record systems, a large number of pre-diagnosed patient data sets are now becoming available. These datasets are often multimodal consisting of images (x-ray, CT, MRI), videos and other time series, and textual data (free text reports and structured clinical data). Analyzing these multimodal sources for disease-specific information across patients can reveal important similarities between patients and hence their underlying diseases and potential treatments. Researchers are now beginning to use techniques of content-based retrieval to search for disease-specific information in images to find supporting evidence for a disease or to automatically learn associations of symptoms and diseases. Benchmarking frameworks such as ImageCLEF (Image retrieval track in the Cross-Language Evaluation Forum) have expanded over the past nine years to include large medical image collections for testing various algorithms for medical image retrieval. This has made comparisons of several techniques for visual, textual, and mixed medical information retrieval as well as for visual classification of medical data possible based on the same data and tasks.
The goal of this workshop is to bring together researchers in medical imaging, medical image retrieval, data mining, text retrieval, and the machine learning/AI communities to discuss new techniques of multimodal mining/retrieval and their use in clinical decision support. We are looking for original, high-quality submissions that address innovative research and development in the analysis, search and retrieval of multimodal medical data for use in clinical decision support. Further, to encourage a larger group of image analysis researchers to profit from the databases and evaluations created in the context of ImageCLEF, groups can get access to ImageCLEF 2011 or 2012 images of the biomedical literature when registering.
Topics of interests include but are not limited to:
Image analysis of visual or multimodal medical data (X-ray, MRI, CT, echo videos, time series data)
Machine learning of disease correlations in visual or multimodal data
Algorithms for indexing and retrieval of data from visual or multimodal medical databases
Disease model-building and clinical decision support systems based on visual or multimodal analysis
Algorithms for medical image retrieval or classification
Systems of retrieval or classification using the ImageCLEF collection
Paper Formatting: Papers are limited to 12 pages. Please use the LNCS Springer kit to format the papers. The workshop chairs reserve the right to reject papers violating the paper length and the formatting instructions outright, without review.
Blind Review: MCBR-CDS reviewing is double blind: authors do not know the names of the reviewers of their papers, and reviewers do not know the names of the authors. Please see the author kit for detailed explanations of how to ensure this.