Charles E. Kahn Jr

Medical College of Wisconsin, Milwaukee, Wisconsin, United States

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Publications (6)0 Total impact

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    Yu Cao, Yili Li, Henning Müller, Charles E Kahn Jr, Ethan Munson
    SPIE Medical Imaging, Orlando, FL, USA; 01/2011
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    ABSTRACT: The seventh edition of the ImageCLEF medical retrieval task was organized in 2010. As in 2008 and 2009, the collection in 2010 uses images and captions from the Radiology and Radiographics journals pub- lished by RSNA (Radiological Society of North America). Three sub- tasks were conducted within the auspices of the medical task: modality detection, image-based retrieval and case-based retrieval. The goal of the modality detection task was to detect the acquisition modality of the images in the collection using visual, textual or mixed methods. The goal of the image-based retrieval task was to retrieve an ordered set of images from the collection that best met the information need specified as a textual statement and a set of sample images, while the goal of the case-based retrieval task was to return an ordered set of articles (rather than images) that best met the information need provided as a description of a "case". The number of registrations to the medical task increased to 51 research groups. However, groups submitting runs have remained stable at 16, with the number of submitted runs increasing to 155. Of these, 61 were ad-hoc runs, 48 were case-based runs while the remaining 46 were modal- ity classification runs. The best results for the ad-hoc retrieval topics were obtained using mixed methods with textual methods also performing well. Textual methods were clearly superior for the case-based topics. For the modality de- tection task, although textual and visual methods alone were relatively successful, combining these techniques proved most effective.
    CLEF 2010 LABs and Workshops, Notebook Papers, 22-23 September 2010, Padua, Italy; 01/2010
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    ABSTRACT: We are investigating question classification for restricted domains with the broader goal of supporting mixed-initiative interaction on mobile phones. In this paper, we discuss the development of a new domain-specific corpus of cancer-related questions and our efforts toward training a classifier. This work includes the development of a new taxonomy of expected answer types that we have been evaluating. Our goal is to create software to engage newly diagnosed prostate cancer patients in question-answering dialogs related to their treatment options. We are focusing our work on the interaction environment afforded by text and multimedia (SMS and MMS) messaging using mobile telephones, because of the prevalence of this technology and the growing popularity of text messaging, especially among underserved populations. This work is interesting from a user interface and communication standpoint because, despite this growing popularity, there has been little formal study of this type of interactive communication.
    ACM International Health Informatics Symposium, IHI 2010, Arlington, VA, USA, November 11 - 12, 2010, Proceedings; 01/2010
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    ABSTRACT: was the sixth year for the ImageCLEF medical retrieval task. Participation was strong again with 38 registered research groups. 17 groups submitted runs and thus participated actively in the tasks. The database in 2009 was similar to the one used in 2008, containing scientific articles from two radiology journals, Radiology and Radiographics. The size of the database was increased to a total of 74,902 images. For each image, captions and access to the full text article through the Medline PMID (PubMed Identifier) were provided. An article's PMID could be used to obtain the officially assigned MeSH (Medical Subject Headings) terms. The collection was entirely in English. However, the topics were, as in previous years, supplied in German, French, and English. Twenty-five image-based topics were provided, of which ten each were visual and mixed and five were textual. In addition, for the first time, 5 case-based topics were provided as an exploratory task. Here the unit of retrieval was intended to be the article and not the image. Case-based topics are designed to be a step closer to the clinical workflow. Clinicians often seek information about patient cases with incomplete information consisting of symptoms, findings, and a set of images. Supplying cases to a clinician from the scientific literature that are similar to the case (s)he is treating can be an important application of image retrieval in the future. As in previous years, most groups concentrated on fully automatic retrieval. How- ever, four groups submitted a total of seven manual or interactive runs. The interactive runs submitted this year performed quite well compared to previous years but did not show a substantial increase in performance over the automatic approaches. In pre- vious years, multimodal combinations were the most frequent submissions. However, this year, as in 2008 only about half as many mixed runs as purely textual runs were submitted. Very few fully visual runs were submitted, and again, the ones submitted performed poorly. The best mean average precisions (MAP) were obtained using auto- matic textual methods. There were mixed feedback runs that had high MAP. The best early precision was also obtained using automatic textual methods, with a few mixed automatic runs also doing well. We had the opportunity to perform multiple judg- ments on some topics. The kappas used as the metric for inter-rater agreement were mostly quite high (¿0.7). However, one of our judges consistently had low kappas as he was significantly more lenient the colleagues. We evaluated the overall performance of groups using strict and lenient judges and found that there was high correlation even though the absolute values for the metrics were different. We also introduced a lung nodule detection task in 2009. This task used the CT slices from the Lung Imaging Data Consortium (LIDC) which included ground truth
    Multilingual Information Access Evaluation II. Multimedia Experiments - 10th Workshop of the Cross-Language Evaluation Forum, CLEF 2009, Corfu, Greece, September 30 - October 2, 2009, Revised Selected Papers; 01/2009
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    ABSTRACT: was the fth year for the medical image retrieval task of ImageCLEF, one of the most popular tracks within CLEF. Participation continued to increase in 2008. A total of 15 groups submitted 111 valid runs. Several requests for data access were also received after the registration deadline. The most signicant change in 2008 was the use of a new database containing images from the medical literature. These images, part of the Goldminer collection, were from the RSNA journals Radiology and Radiographics. Besides the images, the gure captions and the part of the caption referring to a particular sub gure were supplied to the participants. Access to the full text articles in HTML was also provided, as was each article's Medline PMID (PubMed Identier). An article's PMID could be used to obtain the ocially assigned MeSH (Medical Subject Headings) terms. Unlike previous years, this year's collection was entirely in English, as it was obtained from English-language medical literature. However, the topics were, as in previous years, supplied in German, French, and English. The topics used in 2008 were a subset of the 85 topics used in 2005-2007. Thirty topics were made available, ten in each of three categories: visual, mixed, and semantic. As in previous years, most groups concentrated on fully automatic retrieval. How- ever, three groups submitted a total of seven manual or interactive runs; these runs did not show a substantial increase in performance over the automatic approaches. In previous years, multi{modal combinations were the most frequent submissions. How- ever, in 2008 only half as many mixed runs as purely textual runs were submitted. Very few fully visual runs were submitted, and the ones submitted performed poorly. This may be explained in part by the heavily semantic nature of the 2008 topics. The best MAP scores were very similar for textual and multi{modal approaches, whereas early precision performance was clearly better for the multi-modal approaches.
    Evaluating Systems for Multilingual and Multimodal Information Access, 9th Workshop of the Cross-Language Evaluation Forum, CLEF 2008, Aarhus, Denmark, September 17-19, 2008, Revised Selected Papers; 01/2008