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

ImageCLEF 2013: The vision, the data and the open challenges

This paper presents an overview of the ImageCLEF 2013 lab. Since its first edition in 2003, ImageCLEF has become one of the key initiatives promoting the benchmark evaluation of algorithms for the cross-language annotation and retrieval of images in various domains, such as public and personal images, to data acquired by mobile robot platforms and botanic collections. Over the years, by providing new data collections and challenging tasks to the community of interest, the ImageCLEF lab has achieved an unique position in the multi lingual image annotation and retrieval research landscape. The 2013 edition consisted of three tasks: the photo annotation and retrieval task, the plant identification task and the robot vision task. Furthermore, the medical annotation task, that traditionally has been under the ImageCLEF umbrella and that this year celebrates its tenth anniversary, has been organized in conjunction with AMIA for the first time. The paper describes the tasks and the 2013 competition, giving an unifying perspective of the present activities of the lab while discussion the future challenges and opportunities.This work has been partially supported by the Halser Foundation (B. C.),by the LiMoSINe FP7 project under grant # 288024 (B. T.), by the Khresmoi (grant# 257528) and PROMISE ( grant # 258191) FP 7 projects (H.M.) and by the tranScriptorium FP7 project under grant # 600707 (M. V., R. P.)Caputo ., B.; Muller ., H.; Thomee ., B.; Villegas, M.; Paredes Palacios, R.; Zellhofer ., D.; Goeau ., H.... (2013). ImageCLEF 2013: The vision, the data and the open challenges. En Information Access Evaluation. Multilinguality, Multimodality, and Visualization. Springer Verlag (Germany). 8138:250-268. https://doi.org/10.1007/978-3-642-40802-1_26S2502688138Muller, H., Clough, P., Deselaers, T., Caputo, B.: ImageCLEF: experimental evaluation in visual information retrieval. Springer (2010)Tsikrika, T., Seco de Herrera, A.G., Müller, H.: Assessing the scholarly impact of imageCLEF. In: Forner, P., Gonzalo, J., Kekäläinen, J., Lalmas, M., de Rijke, M. (eds.) CLEF 2011. LNCS, vol. 6941, pp. 95–106. Springer, Heidelberg (2011)Huiskes, M., Lew, M.: The MIR Flickr retrieval evaluation. In: Proceedings of the 10th ACM Conference on Multimedia Information Retrieval, Vancouver, BC, Canada, pp. 39–43 (2008)Huiskes, M., Thomee, B., Lew, M.: New trends and ideas in visual concept detection. In: Proceedings of the 11th ACM Conference on Multimedia Information Retrieval, Philadelphia, PA, USA, pp. 527–536 (2010)Villegas, M., Paredes, R.: Overview of the ImageCLEF 2012 Scalable Web Image Annotation Task. In: CLEF 2012 Evaluation Labs and Workshop, Online Working Notes, Rome, Italy (2012)Zellhöfer, D.: Overview of the Personal Photo Retrieval Pilot Task at ImageCLEF 2012. In: CLEF 2012 Evaluation Labs and Workshop, Online Working Notes, Rome, Italy (2012)Villegas, M., Paredes, R., Thomee, B.: Overview of the ImageCLEF 2013 Scalable Concept Image Annotation Subtask. In: CLEF 2013 Evaluation Labs and Workshop, Online Working Notes, Valencia, Spain (2013)Zellhöfer, D.: Overview of the ImageCLEF 2013 Personal Photo Retrieval Subtask. In: CLEF 2013 Evaluation Labs and Workshop, Online Working Notes, Valencia, Spain (2013)Leafsnap (2011)Plantnet (2013)Mobile flora (2013)Folia (2012)Goëau, H., Bonnet, P., Joly, A., Bakic, V., Boujemaa, N., Barthelemy, D., Molino, J.F.: The imageclef 2013 plant identification task. In: ImageCLEF 2013 Working Notes (2013)Pronobis, A., Xing, L., Caputo, B.: Overview of the CLEF 2009 robot vision track. In: Peters, C., Caputo, B., Gonzalo, J., Jones, G.J.F., Kalpathy-Cramer, J., Müller, H., Tsikrika, T. (eds.) CLEF 2009. LNCS, vol. 6242, pp. 110–119. Springer, Heidelberg (2010)Pronobis, A., Caputo, B.: The robot vision task. In: Muller, H., Clough, P., Deselaers, T., Caputo, B. (eds.) ImageCLEF. The Information Retrieval Series, vol. 32, pp. 185–198. Springer, Heidelberg (2010)Pronobis, A., Christensen, H.I., Caputo, B.: Overview of the imageCLEF@ICPR 2010 robot vision track. In: Ünay, D., Çataltepe, Z., Aksoy, S. (eds.) ICPR 2010. LNCS, vol. 6388, pp. 171–179. Springer, Heidelberg (2010)Martinez-Gomez, J., Garcia-Varea, I., Caputo, B.: Overview of the imageclef 2012 robot vision task. In: CLEF 2012 Working Notes (2012)Rusu, R., Cousins, S.: 3d is here: Point cloud library (pcl). In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 1–4. IEEE (2011)Bosch, A., Zisserman, A., Munoz, X.: Image classification using random forests and ferns. In: International Conference on Computer Vision, pp. 1–8. Citeseer (2007)Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. 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The Springer International Series on Information Retrieval, vol. 32. Springer, Heidelberg (2010)Kalpathy-Cramer, J., Müller, H., Bedrick, S., Eggel, I., García Seco de Herrera, A., Tsikrika, T.: The CLEF 2011 medical image retrieval and classification tasks. In: Working Notes of CLEF 2011 (Cross Language Evaluation Forum) (2011)Müller, H., García Seco de Herrera, A., Kalpathy-Cramer, J., Demner Fushman, D., Antani, S., Eggel, I.: Overview of the ImageCLEF 2012 medical image retrieval and classification tasks. In: Working Notes of CLEF 2012 (Cross Language Evaluation Forum) (2012)García Seco de Herrera, A., Kalpathy-Cramer, J., Demner Fushman, D., Antani, S., Müller, H.: Overview of the ImageCLEF 2013 medical tasks. In: Working Notes of CLEF 2013 (Cross Language Evaluation Forum) (2013

ImageCLEF 2014: Overview and analysis of the results

This paper presents an overview of the ImageCLEF 2014 evaluation lab. Since its first edition in 2003, ImageCLEF has become one of the key initiatives promoting the benchmark evaluation of algorithms for the annotation and retrieval of images in various domains, such as public and personal images, to data acquired by mobile robot platforms and medical archives. Over the years, by providing new data collections and challenging tasks to the community of interest, the ImageCLEF lab has achieved an unique position in the image annotation and retrieval research landscape. The 2014 edition consists of four tasks: domain adaptation, scalable concept image annotation, liver CT image annotation and robot vision. This paper describes the tasks and the 2014 competition, giving a unifying perspective of the present activities of the lab while discussing future challenges and opportunities.This work has been partially supported by the tranScriptorium FP7 project under grant #600707 (M. V., R. 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Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis

The availability of large-scale annotated image datasets and recent advances in supervised deep learning methods enable the end-to-end derivation of representative image features that can impact a variety of image analysis problems. Such supervised approaches, however, are difficult to implement in the medical domain where large volumes of labelled data are difficult to obtain due to the complexity of manual annotation and inter- and intra-observer variability in label assignment. We propose a new convolutional sparse kernel network (CSKN), which is a hierarchical unsupervised feature learning framework that addresses the challenge of learning representative visual features in medical image analysis domains where there is a lack of annotated training data. Our framework has three contributions: (i) We extend kernel learning to identify and represent invariant features across image sub-patches in an unsupervised manner. (ii) We initialise our kernel learning with a layer-wise pre-training scheme that leverages the sparsity inherent in medical images to extract initial discriminative features. (iii) We adapt a multi-scale spatial pyramid pooling (SPP) framework to capture subtle geometric differences between learned visual features. We evaluated our framework in medical image retrieval and classification on three public datasets. Our results show that our CSKN had better accuracy when compared to other conventional unsupervised methods and comparable accuracy to methods that used state-of-the-art supervised convolutional neural networks (CNNs). Our findings indicate that our unsupervised CSKN provides an opportunity to leverage unannotated big data in medical imaging repositories.Comment: Accepted by Medical Image Analysis (with a new title 'Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis'). The manuscript is available from following link (https://doi.org/10.1016/j.media.2019.06.005

Experiences from the ImageCLEF Medical Retrieval and Annotation Tasks

The medical tasks in ImageCLEF have been run every year from 2004-2018 and many different tasks and data sets have been used over these years. The created resources are being used by many researchers well beyond the actual evaluation campaigns and are allowing to compare the performance of many techniques on the same grounds and in a reproducible way. Many of the larger data sets are from the medical literature, as such images are easier to obtain and to share than clinical data, which was used in a few smaller ImageCLEF challenges that are specifically marked with the disease type and anatomic region. This chapter describes the main results of the various tasks over the years, including data, participants, types of tasks evaluated and also the lessons learned in organizing such tasks for the scientific community

ImageCLEF 2019: Multimedia Retrieval in Lifelogging, Medical, Nature, and Security Applications

This paper presents an overview of the foreseen ImageCLEF 2019 lab that will be organized as part of the Conference and Labs of the Evaluation Forum - CLEF Labs 2019. ImageCLEF is an ongoing evaluation initiative (started in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2019, the 17th edition of ImageCLEF will run four main tasks: (i) a Lifelog task (videos, images and other sources) about daily activities understanding, retrieval and summarization, (ii) a Medical task that groups three previous tasks (caption analysis, tuberculosis prediction, and medical visual question answering) with newer data, (iii) a new Coral task about segmenting and labeling collections of coral images for 3D modeling, and (iv) a new Security task addressing the problems of automatically identifying forged content and retrieve hidden information. The strong participation, with over 100 research groups registering and 31 submitting results for the tasks in 2018 shows an important interest in this benchmarking campaign and we expect the new tasks to attract at least as many researchers for 2019

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

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