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Multi-modal skin lesion diagnosis (MSLD) has achieved remarkable success bymodern computer-aided diagnosis (CAD) technology based on deep convolutions.However, the information aggregation across modalities in MSLD remainschallenging due to severity unaligned spatial resolution (e.g., dermoscopicimage and clinical image) and heterogeneous data (e.g., dermoscopic image andpatients' meta-data). Limited by the intrinsic local attention, most recentMSLD pipelines using pure convolutions struggle to capture representativefeatures in shallow layers, thus the fusion across different modalities isusually done at the end of the pipelines, even at the last layer, leading to aninsufficient information aggregation. To tackle the issue, we introduce a puretransformer-based method, which we refer to as ``Throughout Fusion Transformer(TFormer)'', for sufficient information integration in MSLD. Different from theexisting approaches with convolutions, the proposed network leveragestransformer as feature extraction backbone, bringing more representativeshallow features. We then carefully design a stack of dual-branch hierarchicalmulti-modal transformer (HMT) blocks to fuse information across different imagemodalities in a stage-by-stage way. With the aggregated information of imagemodalities, a multi-modal transformer post-fusion (MTP) block is designed tointegrate features across image and non-image data. Such a strategy thatinformation of the image modalities is firstly fused then the heterogeneousones enables us to better divide and conquer the two major challenges whileensuring inter-modality dynamics are effectively modeled.

TFormer: A throughout fusion transformer for multi-modal skin lesion diagnosis