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Full Description
This book constitutes the refereed proceedings of the Third MICCAI Workshop on Medical Content-Based Retrieval for Clinical Decision Support, MCBR-CBS 2012, held in Nice, France, in October 2012.
The 10 revised full papers presented together with 2 invited talks were carefully reviewed and selected from 15 submissions. The papers are divided on several topics on 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.
Contents
Workshop Overview.- Overview of the Third Workshop on Medical Content-Based Retrieval for Clinical Decision Support (MCBR-CDS 2012).- Invited Talk.- A Polynomial Model of Surgical Gestures for Real-Time Retrieval of Surgery Videos.- Methods.- Exploiting 3D Part-Based Analysis, Description and Indexing to Support Medical Applications.- Skull Retrieval for Craniosynostosis Using Sparse Logistic Regression Models.- 3D/4D Data Retrieval.- Retrieval of 4D Dual Energy CT for Pulmonary Embolism Diagnosis.- Immediate ROI Search for 3-D Medical Images.- The Synergy of 3D SIFT and Sparse Codes for Classification of Viewpoints from Echocardiogram Videos.- Assessing the Classification of Liver Focal Lesions by Using Multi-phase Computer Tomography Scans.- Invited Talk.- VISCERAL: Towards Large Data in Medical Imaging — Challenges and Directions.- Visual Features.- Customised Frequency Pre-filtering in a Local Binary Pattern-Based Classification of Gastrointestinal Images.- Bag-of-Colors for Biomedical Document Image Classification.- Multimodal Retrieval.- An SVD-Bypass Latent Semantic Analysis for Image Retrieval.- Multimedia Retrieval in a Medical Image Collection: Results Using Modality Classes.
