Image Captioning is a field that lies at the intersection of Computer Vision (CV) and Natural
Language Processing (NLP). It concerns the automatic generation of a brief textual …

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 …

Over the last two decades, medical imaging examinations, and technologies together have
been exponentially increased. With the increased demand for medical examinations, the …

The University of Essex participated in the fourth edition of the ImageCLEFcaption task
which aims to detect concepts on radiology images as an approach to medical image …

Publications in biomedical research are increasing in number so fast that it is nearly
impossible for scholars and professionals to keep track of new developments or even search …

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 …

This paper introduces RTEx, a novel methodology for a) ranking radiography exams based
on their probability to contain an abnormality, b) generating abnormality tags for abnormal …

In general, there are two open challenges for domain specific visual concept detection. First
is the high intra-class variations and second is to collect large collection of sample training …

Despite the high number of machine learning models presented in the last few years for
automatically annotating medical images with deep learning models, clear baselines to …

Medical image classi cation is a very challenging and interesting task that has seen
incredible advancements with the use of deep learning approaches. This thesis addresses …