Paper Title
MULTISIMILARITYATTENTION-BASED EVALUATION METRICS FOR SYNTHETIC MELANOMA IMAGES

Abstract
Abstract - Melanoma, a lethal form of skin cancer, necessitates early detection and precise diagnosis for effective treatment. Machine learning models, though technologically advanced, heavily rely on training data availability. Given the scarcity of authentic melanoma data, synthetic images offer a valuable solution for training diagnostic models. However, evaluating the practical utility of these synthetic images, especially for medical and skin cancer applications, remains challenging. Traditional evaluation methods fall short, particularly in distinguishing subtle changes in small lesion pixels that may elude human dermatologists. A multi-view, multi-evaluation approach is crucial, surpassing mere distribution matching. Recognizing the absence of visualization during generative model training, our innovative method introduces a Multi Similarity Attention mechanism to enhance evaluation metrics for synthetic melanoma images. Through extensive experiments, we demonstrate the effectiveness of our methodology by visually simulating synthetic images alongside real ones during the training process. Keywords: Deep learning, GAN, Image Similarity, Skin Cancer