This paper presents an overview of the ImageCLEF 2017 evaluation campaign, an event
that was organized as part of the CLEF (Conference and Labs of the Evaluation Forum) labs …

Manual image annotation is a major bottleneck in the processing of medical images and the
accuracy of these reports varies depending on the clinician's expertise. Automating some or …

Medical images are often used in clinical diagnosis. However, interpreting the insights
gained from them is often a time-consuming task even for experts. For this reason, there is a …

In this paper we present the methods and techniques performed by the IPL Group for the
concept detection task of ImageCLEF 2017. A probabilistic k-nearest neighbor approach …

This paper describes the participation of the US National Library of Medicine (NLM) in the
ImageCLEF 2017 caption task. We proposed different machine learning methods using …

This paper presents the modeling approaches performed by the FHDO Biomedical
Computer Science Group (BCSG) for the caption prediction task at ImageCLEF 2017. The …

Representation learning is a field that has rapidly evolved during the last decade, with much
of this progress being driven by the latest breakthroughs in deep learning. Digital medical …

This article describes the participation of the Computer Science Department of Morgan State
University, Baltimore, Maryland, USA in the ImageCLEFcaption under ImageCLEF 2017 …

In this report, we summarize our solution to the ImageCLEF 2017 caption detection task.
ImageCLEF's concept detection task provides a testbed for figure caption prediction oriented …

In this paper, we describe the participation of the Mami team at ImageCLEF 2017 for the
Image Caption task. We participated to the concept detection subtask which aims at …