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Most convolutional neural network (CNN) based methods for skin cancerclassification obtain their results using only dermatological images. Althoughgood classification results have been shown, more accurate results can beachieved by considering the patient's metadata, which is valuable clinicalinformation for dermatologists. Current methods only use the simple jointfusion structure (FS) and fusion modules (FMs) for the multi-modalclassification methods, there still is room to increase the accuracy byexploring more advanced FS and FM. Therefore, in this paper, we design a newfusion method that combines dermatological images (dermoscopy images orclinical images) and patient metadata for skin cancer classification from theperspectives of FS and FM. First, we propose a joint-individual fusion (JIF)structure that learns the shared features of multi-modality data and preservesspecific features simultaneously. Second, we introduce a fusion attention (FA)module that enhances the most relevant image and metadata features based onboth the self and mutual attention mechanism to support the decision-makingpipeline. We compare the proposed JIF-MMFA method with other state-of-the-artfusion methods on three different public datasets. The results show that ourJIF-MMFA method improves the classification results for all tested CNNbackbones and performs better than the other fusion methods on the three publicdatasets, demonstrating our method's effectiveness and robustness

Joint-Individual Fusion Structure with Fusion Attention Module for Multi-Modal Skin Cancer Classification