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Convolutional neural networks (CNNs) have achieved the state-of-the-artperformance in skin lesion analysis. Compared with single CNN classifier,combining the results of multiple classifiers via fusion approaches shows to bemore effective and robust. Since the skin lesion datasets are usually limitedand statistically biased, while designing an effective fusion approach, it isimportant to consider not only the performance of each classifier on thetraining/validation dataset, but also the relative discriminative power (e.g.,confidence) of each classifier regarding an individual sample in the testingphase, which calls for an active fusion approach. Furthermore, in skin lesionanalysis, the data of certain classes (e.g., the benign lesions) is usuallyabundant making them an over-represented majority, while the data of some otherclasses (e.g., the cancerous lesions) is deficient, making them anunderrepresented minority. It is more crucial to precisely identify the samplesfrom an underrepresented (i.e., in terms of the amount of data) but moreimportant minority class (e.g., certain cancerous lesion). In other words,misclassifying a more severe lesion to a benign or less severe lesion shouldhave relative more cost (e.g., money, time and even lives). To address suchchallenges, we present CS-AF, a cost-sensitive multi-classifier active fusionframework for skin lesion classification. In the experimental evaluation, weprepared 96 base classifiers (of 12 CNN architectures) on the ISIC researchdatasets. Our experimental results show that our framework consistentlyoutperforms the static fusion competitors.

CS-AF: A Cost-sensitive Multi-classifier Active Fusion Framework for Skin Lesion Classification