Medical vision-language models (VLMs) combine computer vision and natural language
processing to analyze visual and textual medical data. Our paper reviews recent …

Medical visual question answering (VQA) is a challenging task that requires answering
clinical questions of a given medical image, by taking consider of both visual and language …

This paper presents an overview of the ImageCLEF 2022 lab that was organized as part of
the Conference and Labs of the Evaluation Forum–CLEF Labs 2022. ImageCLEF is an …

Medical image visual question answering (VQA) is a task to answer clinical questions, given
a radiographic image, which is a challenging problem that requires a model to integrate both …

Résumé The 2023 ImageCLEFmedical GANs task is the first edition of this task, examining
the existing hypothesis that GANs (Generative Adversarial Networks) are generating …

Automated medical image analysis systems often require large amounts of training data with
high quality labels, which are difficult and time consuming to generate. This paper …

We present the methods AUEB's NLP Group used to participate in the annual
ImageCLEFmedical Caption Task. The task comprises of the Concept Detection and the …

In this paper, we provide an overview of the upcoming ImageCLEF campaign. ImageCLEF is
part of the CLEF Conference and Labs of the Evaluation Forum since 2003. ImageCLEF, the …

In scientific publications, a substantial part of the information is expressed via figures
containing images and diagrams. Hence, the retrieval of relevant figures given a natural …

Diagnostic Captioning is described as the automatic text generation from a collection of X-
RAY images and it can assist inexperienced doctors and radiologists to reduce clinical …