UNED-UV at Medical Retrieval Task of ImageCLEF 2011

This paper describes the participation of DAEDALUS at ImageCLEF 2011 Medical Retrieval task. We have focused on multimodal (or mixed) experiments that combine textual and visual retrieval. The main objective of our research has been to evaluate the effect on the medical retrieval process of the existence of an extended corpus that is annotated with the image type, associated to both the image itself and also to its textual description. For this purpose, an image classifier has been developed to tag each document with its class (1st level of the hierarchy: Radiology, Microscopy, Photograph, Graphic, Other) and subclass (2nd level: AN, CT, MR, etc.). For the textual-based experiments, several runs using different semantic expansion techniques have been performed. For the visual-based retrieval, different runs are defined by the corpus used in the retrieval process and the strategy for obtaining the class and/or subclass. The best results are achieved in runs that make use of the image subclass based on the classification of the sample images. Although different multimodal strategies have been submitted, none of them has shown to be able to provide results that are at least comparable to the ones achieved by the textual retrieval alone. We believe that we have been unable to find a metric for the assessment of the relevance of the results provided by the visual and textual processes.

clef.isti.cnr.it

The main goal of this paper is to present our experiments in the classification modality and in the ad-hoc image retrieval tasks with the Medical collection at ImageCLEF 2012 Campaign. This edition we focus on applying new strategies for both the textual and the visual subsystems included in our multimodal retrieval system. The visual subsystem has focus on extending the low-level features vector with concept features. These concept features have been calculated by means of a logistic regression model. The textual subsystem has focus on applying a query reformulation to remove general and domain stop-words, trying to produce a query with only medical-related terms. We have not obtained the results as good as obtained at the Photo annotation retrieval subtask using similar techniques. Therefore, a deep analysis for the Medical collection will be done.

… Working Notes. CLEF …

This article describes the participation of the Image and Text Integration (ITI) group from the United States National Library of Medicine (NLM) in the ImageCLEF 2009 medical retrieval track. Our methods encompass a variety of techniques relating to document ...

This paper presents the 2005 MIRACLE's team participation in the ImageCLEFmed task of ImageCLEF 2005. This task certainly requires the use of image retrieval techniques and therefore it is mainly aimed at image analysis research groups. Although our areas of expertise don't include image analysis research, we decided to make the effort to participate in this task to promote and encourage multidisciplinary participation in all aspects of information retrieval, no matter if it is text or content based. We resort to a publicly available image retrieval system (GIFT [1]) when needed.

2005

This paper presents the 2005 MIRACLE’s team participation in the ImageCLEFmed task of ImageCLEF 2005. This task certainly requires the use of image retrieval techniques and therefore it is mainly aimed at image analysis research groups. Although our areas of expertise don’t include image analysis research, we decided to make the effort to participate in this task to promote and encourage multidisciplinary participation in all aspects of information retrieval, no matter if it is text or content based. We resort to a publicly available image retrieval system (GIFT [1]) when needed.

Working Notes for …, 2006

This paper presents the contribution of IPAL group on the CLEF 2006 medical retrieval task (i.e. ImageCLEFmed). The main idea of our group is to incorporate medical knowledge in the retrieval system within a multimodal fusion framework. For text, this knowledge is in the Unified Medical Language System (UMLS) sources. For images, this knowledge is in semantic features that are learned from examples within structured learning framework. We propose to represent both image and text using UMLS concepts. The use of UMLS concepts allows the system to work at a higher semantic level and to standardize the semantic index of medical data, facilitating the communication between visual end textual indexing and retrieval. The results obtained with UMLS-based approaches show the potential of this conceptual indexing, especially when using a semantic dimension filtering, and the benefit of working within a fusion framework, leading to the best results of ImageCLEFmed 2006. We also test a visual retrieval system based on manual query design and visual task fusion. Even if it provides the best visual results, this purely visual retrieval provides poor results in comparison to the best textual approaches.

Evaluating Systems for …, 2009

Lecture Notes in Computer Science, 2005

This paper presents the results of the State University of New York at Buffalo in the cross-language medical image retrieval task at CLEF 2004. Our work in image retrieval explores the combination of image and text retrieval using automatic query expansion. The system uses pseudo relevance feedback on the case descriptions associated with the top 10 images to improve ranking of images retrieved by a CBIR system. The results show significant improvements with respect to a base line that uses only image retrieval.

Computerized Medical Imaging and Graphics, 2014

Medical image retrieval and classification have been extremely active research topics over the past 15 years. With the ImageCLEF benchmark in medical image retrieval and classification a standard test bed was created that allows researchers to compare their approaches and ideas on increasingly large and varied data sets including generated ground truth. This article describes the lessons learned in ten evaluations campaigns. A detailed analysis of the data also highlights the value of the resources created. Introduction: While development of image retrieval approaches and systems began as a research field over two decades ago , progress has been slow for a variety of reasons. One problem is the inability of image processing algorithms to automatically identify the content of images in the manner that information retrieval and extraction systems have been able to do so with text . A second problem is the lack of robust test collections and in particular, realistic query tasks with ground truth that allow comparison of system performance . In general, the limits of systematic comparisons have been analyzed in several publications , but also an important impact could be shown when evaluating the results of such benchmarks , particularly economic value but also scholarly impact in terms of citations. The lack of realistic test collections is one of the motivations for the ImageCLEF initiative, which is a part of the Cross--Language Evaluation Forum (CLEF), a challenge evaluation for information retrieval from diverse languages . The goals of CLEF are to build realistic test collections that simulate real world retrieval tasks, enable researchers to assess the performance of their systems, and compare their results with others. The goal of test collection construction is to assemble a large collection of content (text, images, structured data, etc.) that resemble collections used in the real world. Builders of test collections also seek a sample of realistic tasks to serve as topics that can be submitted to systems as queries to retrieve content . The final component of test collections is relevance judgments that determine which content is considered relevant to each topic. Biomedical information retrieval systems are complex, comprising many key components. These include image modality classification , visual image similarity computation, multimodal image and text information retrieval, and others that may be specific to individual systems. Performance evaluation needs to be conducted on these components to determine the overall system performance. With the exponential increase in available biomedical data repositories it is important for the evaluation to be close to real world in its size and scope. The ImageCLEF 1 medical retrieval tasks have provided such an 1 http://www.imageclef.org/ evaluation forum and framework for evaluating the state of the art in biomedical image information retrieval .