Overview of the ImageCLEF 2013 medical tasks

In 2013, the tenth edition of the medical task of the Image-CLEF benchmark was organized. For the first time, the ImageCLEFmedworkshop takes place in the United States of America at the annualAMIA (American Medical Informatics Association) meeting even thoughthe task was organized as in previous years in connection with the otherImageCLEF tasks. Like 2012, a subset of the open access collection ofPubMed Central was distributed. This year, there were four subtasks:modality classification, compound figure separation, image–based andcase–based retrieval. The compound figure separation task was includeddue to the large number of multipanel images available in the literatureand the importance to separate them for targeted retrieval. More com-pound figures were also included in the modality classification task tomake it correspond to the distribution in the full database. The retrievaltasks remained in the same format as in previous years but a largernumber of tasks were available for image–based and case–based tasks.This paper presents an analysis of the techniques applied by the tengroups participating 2013 in ImageCLEFmed

The medGIFT Group in ImageCLEFmed 2013

This article presents the participation of the medGIFT groupin ImageCLEFmed 2013. Since 2004, the group has participated in themedical image retrieval tasks of ImageCLEF each year. There are fourtypes of tasks for ImageCLEFmed 2013: modality classi cation, image{based retrieval, case{based retrieval and a new task on compound gureseparation. The medGIFT group participated in all four tasks. MedGIFTis developing a system named ParaDISE (Parallel Distributed ImageSearch Engine), which is the successor of GIFT (GNU Image FindingTool). The alpha version of ParaDISE was used to run the experimentsin the competition. The focus was on the use of multiple features incombinations with novel strategies, i.e, compound gure separation formodality classi cation or modality ltering for ad{hoc image and case{based retrieval

Medical image modality classification using discrete Bayesian Networks

In this paper we propose a complete pipeline for medical image modality classification focused on the application of discrete Bayesian network classifiers. Modality refers to the categorization of biomedical images from the literature according to a previously defined set of image types, such as X-ray, graph or gene sequence. We describe an extensive pipeline starting with feature extraction from images, data combination, pre-processing and a range of different classification techniques and models. We study the expressive power of several image descriptors along with supervised discretization and feature selection to show the performance of discrete Bayesian networks compared to the usual deterministic classifiers used in image classification. We perform an exhaustive experimentation by using the ImageCLEFmed 2013 collection. This problem presents a high number of classes so we propose several hierarchical approaches. In a first set of experiments we evaluate a wide range of parameters for our pipeline along with several classification models. Finally, we perform a comparison by setting up the competition environment between our selected approaches and the best ones of the original competition. Results show that the Bayesian Network classifiers obtain very competitive results. Furthermore, the proposed approach is stable and it can be applied to other problems that present inherent hierarchical structures of classes

Evaluating performance of biomedical image retrieval systems - an overview of the medical image retrieval task at ImageCLEF 2004-2013

Medical image retrieval and classification have been extremely active research topics over the past 15 years. Within 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 evaluation campaigns. A detailed analysis of the data also highlights the value of the resources created

Use Case Oriented Medical Visual Information Retrieval & System Evaluation

Large amounts of medical visual data are produced daily in hospitals, while new imaging techniques continue to emerge. In addition, many images are made available continuously via publications in the scientific literature and can also be valuable for clinical routine, research and education. Information retrieval systems are useful tools to provide access to the biomedical literature and fulfil the information needs of medical professionals. The tools developed in this thesis can potentially help clinicians make decisions about difficult diagnoses via a case-based retrieval system based on a use case associated with a specific evaluation task. This system retrieves articles from the biomedical literature when querying with a case description and attached images. This thesis proposes a multimodal approach for medical case-based retrieval with focus on the integration of visual information connected to text. Furthermore, the ImageCLEFmed evaluation campaign was organised during this thesis promoting medical retrieval system evaluation

Medical Image Retrieval Using Multimodal Semantic Indexing

Large collections of medical images have become a valuable source of knowledge, taking an important role in education, medical research and clinical decision making. An important unsolved issue that is actively investigated is the efficient and effective access to these repositories. This work addresses the problem of information retrieval in large collections of biomedical images, allowing to use sample images as alternative queries to the classic keywords. The proposed approach takes advantage of both modalities: text and visual information. The main drawback of the multimodal strategies is that the associated algorithms are memory and computation intensive. So, an important challenge addressed in this work is the design of scalable strategies, that can be applied efficiently and effectively in large medical image collections. The experimental evaluation shows that the proposed multimodal strategies are useful to improve the image retrieval performance, and are fully applicable to large image repositories.Maestrí