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Enhanced biomedical image tagging
(2024-09-15) Chatzipapadopoulou, Anna; Χατζηπαπαδοπούλου, Άννα; Androutsopoulos, Ion; Pavlopoulos, John
Medical image classification is a complex and fascinating task that has significantly benefited from advancements in deep learning techniques. This thesis focuses on the specific challenge of multi-class multi-label medical image classification, also known as medical image tagging. The objective is to accurately assign relevant medical terms (concepts/tags) to images, which describe potential findings and ultimately assist clinicians in the diagnostic process. Medical image tagging may involve analyzing images from various modalities, such as X-rays, MRIs, CT scans, and ultra sonographies, to identify and categorize different pathological conditions. To address the task of medical image tagging, we develop and evaluate various deep learning models that encode the images and combine them with both classification-based and retrieval-based approaches. Ultimately, our goal is to develop a robust and reliable system for medical image tagging that not only provides clinicians with supportive information but also assists them in diagnosing diseases more accurately and efficiently.