The goal of this workshop is to bring together researchers in medical imaging, medical image retrieval, data mining, text retrieval, and 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 2011 images of the biomedical literature when registering.
All accepted papers will be published in a volume of the SPRINGER LECTURE NOTES in Computer Science (LNCS).
Important Dates
Camera ready copy : August 27th, 2011
Workshop date: September 22nd 2011
08.30 - 08:45 Opening
08:50 - 09:50 Keynote Lecture 1: Nicholas Ayache, Research Director, INRIA Sophia Antipolis - Méditerranée, France.
Title, Content-Based Retrieval in Endomicroscopy:
Toward an Efficient Smart Atlas for Clinical Diagnosis.
09:50 - 10:30
Histology Image Indexing using a Non-negative Semantic Embedding
Jorge Vanegas, Juan Caicedo, Fabio González, Eduardo Romero;
National University of Colombia
Texture Bags: Anomaly Retrieval in Medical Images based on Local 3D-Texture Similarity
Andreas Burner, Rene Donner, Marius Mayerhoefer, Franz Kainberger, Georg Langs ;
Medical University of Vienna
10:30 - 10:50 Coffee break
10:50 - 12:00
A Discriminative Distance Learning-Based CBIR Framework for Characterization of Indeterminate Liver Lesions
Maria Costa, Alexey Tsymbal, Michael Suehling, Dorin Comaniciu;
Siemens AG
Using Multiscale Visual Words for Lung Texture Classification and Retrieval
Antonio Foncubierta-Rodriguez, Adrien Depeursinge, Henning Müller;
University of Applied Sciences Western Switzerland (HES-SO)
Superpixel-based Interest Points for Effective Bags of Visual Words Medical Image Retrieval
Sebastian Haas, Rene Donner;
Medical University of Vienna
12:00 - 13:00 Lunch
13:15 - 14:15 Keynote Lecture II : Dorin Comaniciu, Global Technology Head, Siemens Corporate Research, Princeton, NJ
14:15 - 15:15
Computer--Aided Diagnosis of Pigmented Skin Dermoscopic Images
Asad Safi, Maximilian Baust, Olivier Pauly, Victor
Castaneda, Tobias Lasser, Diana Mateus, Nassir Navab, Rüdliger Hein,Mahzad Ziai;
Technical University of Munich
Semantic Analysis of 3D Anatomical Medical Images for Sub-Image Retrieval
Vikram Venkatraghavan, Indian Institute of Technology, Kharagpur;
Sohan Ranjan, General Electric
A Fast 2D and 3D Medical Image Retrieval Approach using Miniature Alignment and Random Projections
Rene Donner, Sebastian Haas, Andreas Burner, Georg Langs
Medical University Vienna
15:15 - 15:45 Coffee break
15:45-16:45
Biomedical Image Retrieval using Multimodal Context and Concept Feature Spaces
Mahmudur Rahman, Sameer Anatani, George Thoma, Dina Fushman;
National Library of Medicine(NLM), NIH
Building Implicit Dictionaries based on Extreme Random Clustering for Modality Recognition
Olivier Pauly, Diana Mateus, Nassir Navab;
Technical University of Munich
Using MeSH to Expand Queries in Medical Image Retrieval
Jacinto Mata, Mariano Crespo, Manuel J. Maña;
University of Huelva, Spain
16:45 - 17:45
PANEL + Open Discussion: What is the CBIR role in Medical Decision Support?
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 modalities 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 eight 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 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 images of the biomedical literature when registering.
Topics of interests include but are not limited to:
- Image analysis of multimodal medical data (X-ray, MRI, CT, echo videos, time series data)
- Machine learning of disease correlations in multimodal data
- Algorithms for indexing and retrieval of data from multimodal medical databases
- Disease model-building and clinical decision support systems based on multimodal analysis
- Algorithms for medical image retrieval or classification
- Systems of retrieval or classification using the ImageCLEF collection
Program chairs
Program Committee
Burak Acar, Dept. of Electrical Eng., Bogazici University, Turkey
Amir Amini, Dept of Electrical and Computer Eng., University of Louisville, USA
Sameer Anatani, National Library of Medicine (NLM), USA
Rahul Bhotika, GE Global Research Center, NY, USA
Albert Chung, Medical Image Analysis Lab., CS and Eng., Hong Kong University of Science and Technology, Hong Kong
Antonio Criminisi, Microsoft Research
Thomas M.Deserno, Medical Informatics Dept., Aachen University of Technology (RWTH), Germany
Gerhard Engelbrecht, Comp. Imaging and Simulation in Biomedicine (CISTIB), University Pompeu Fabra (UPF), Spain
Bram van Ginneken, Diagnostic Image Analysis Group of Radboud University Nijmegen Medical Centre, The Netherlands
Allan Hanbury, Information Retrieval Facility (IRF) and Institute of CAD, Vienna University of Tech., Austria.
Nico Karssemeijer, Radiology Dept., Radboud Univ. Nijmegen, The Netherlands
Jayashree Kalpathy-Cramer, Medical Informatics & Clinical Epidemiology, OHSU, USA
Georg Langs, Medical Vision Group, CSAIL, MIT, USA
Yanxi Liu, CS and Eng.,UPENN, USA
Rodney Long, NLM, USA
Robert Lundstrum, MD, Kaiser Permanente, San-Francisco Medical center, USA
Kazunori Okada, CS Dept, SFSU, USA,
Daniel Racoceanu, French National Center for Scientific Research (CNRS), France
Daniel Rubin, Radiology Dept. Stanford, USA
Linda Shapiro, CS and Eng., Biomedical Informatics, University of Washington, USA
Ron Summers, Clinical Image Processing, Imaging Biomarkers and Computer-Aided Diagnosis, NIH, USA
Agma Traina, CS Dept., University of Sao Paulo, Brazil
Pingkun Yan, Center for Optical Imagery Analysis and Learning, Chinese Academy of Sciences, China
S. Kevin Zhou, Siemens Corporate Research, USA
