Abstract The 2024 ImageCLEFmedical GANs task Controlling the Quality of Synthetic
Medical Images created via GANs is in its second edition. It comprises two sub-tasks which …

Multimodal processing and language generation require models to internally represent both
language and vision, and then generate contextually appropriate responses. To do so with …

Purpose: This study focuses on the development of automated text generation from
radiology images, termed diagnostic captioning, to assist medical professionals in reducing …

This working note documents the participation of CS_Morgan in the ImageCLEFmedical
2024 Caption subtasks, focusing on Caption Prediction and Concept Detection challenges …

This paper describes KDE Lab approach in ImageCLEF GANs 2024. ImageCLEFmedical
GANs 2024 task consists of two sub tasks, Identify training data fingerprints task and Detect …

Abstract 2024 marks the 25 th birthday for CLEF, an evaluation campaign activity which has
applied the Cranfield evaluation paradigm to the testing of multilingual and multimodal …

This paper presents the participation of AI Multimedia Lab to the 2024 ImageCLEFmedical
GANs task. The 2024 ImageCLEFmedical GANs propose two sub-tasks that study security …

This article describes the approaches that the AUEB NLP Group experimented with during
its participation in the 8th edition of the ImageCLEFmedical Caption evaluation campaign …

Abstract CLEF 2024 ImageCLEF Gans Task is an example of the challenging research
problem in the field of CT image analysis. The purpose of this research is to detect the …

Automatically generating captions and reports for medical images has become increasingly
important due to the growing workload of radiologists in hospitals. To tackle this challenging …