  Most convolutional neural network (CNN) based methods for skin cancer
classification obtain their results using only dermatological images. Although
good classification results have been shown, more accurate results can be
achieved by considering the patient's metadata, which is valuable clinical
information for dermatologists. Current methods only use the simple joint
fusion structure (FS) and fusion modules (FMs) for the multi-modal
classification methods, there still is room to increase the accuracy by
exploring more advanced FS and FM. Therefore, in this paper, we design a new
fusion method that combines dermatological images (dermoscopy images or
clinical images) and patient metadata for skin cancer classification from the
perspectives of FS and FM. First, we propose a joint-individual fusion (JIF)
structure that learns the shared features of multi-modality data and preserves
specific features simultaneously. Second, we introduce a fusion attention (FA)
module that enhances the most relevant image and metadata features based on
both the self and mutual attention mechanism to support the decision-making
pipeline. We compare the proposed JIF-MMFA method with other state-of-the-art
fusion methods on three different public datasets. The results show that our
JIF-MMFA method improves the classification results for all tested CNN
backbones and performs better than the other fusion methods on the three public
datasets, demonstrating our method's effectiveness and robustness
