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Many skin lesion analysis (SLA) methods recently focused on developing amulti-modal-based multi-label classification method due to two factors. Thefirst is multi-modal data, i.e., clinical and dermoscopy images, which canprovide complementary information to obtain more accurate results thansingle-modal data. The second one is that multi-label classification, i.e.,seven-point checklist (SPC) criteria as an auxiliary classification task cannot only boost the diagnostic accuracy of melanoma in the deep learning (DL)pipeline but also provide more useful functions to the clinical doctor as it iscommonly used in clinical dermatologist's diagnosis. However, most methods onlyfocus on designing a better module for multi-modal data fusion; few methodsexplore utilizing the label correlation between SPC and skin disease forperformance improvement. This study fills the gap that introduces a GraphConvolution Network (GCN) to exploit prior co-occurrence between each categoryas a correlation matrix into the DL model for the multi-label classification.However, directly applying GCN degraded the performances in our experiments; weattribute this to the weak generalization ability of GCN in the scenario ofinsufficient statistical samples of medical data. We tackle this issue byproposing a Graph-Ensemble Learning Model (GELN) that views the prediction fromGCN as complementary information of the predictions from the fusion model andadaptively fuses them by a weighted averaging scheme, which can utilize thevaluable information from GCN while avoiding its negative influences as much aspossible. To evaluate our method, we conduct experiments on public datasets.The results illustrate that our GELN can consistently improve theclassification performance on different datasets and that the proposed methodcan achieve state-of-the-art performance in SPC and diagnosis classification.

Graph-Ensemble Learning Model for Multi-label Skin Lesion Classification using Dermoscopy and Clinical Images