  Convolutional neural networks (CNNs) have achieved the state-of-the-art
performance in skin lesion analysis. Compared with single CNN classifier,
combining the results of multiple classifiers via fusion approaches shows to be
more effective and robust. Since the skin lesion datasets are usually limited
and statistically biased, while designing an effective fusion approach, it is
important to consider not only the performance of each classifier on the
training/validation dataset, but also the relative discriminative power (e.g.,
confidence) of each classifier regarding an individual sample in the testing
phase, which calls for an active fusion approach. Furthermore, in skin lesion
analysis, the data of certain classes (e.g., the benign lesions) is usually
abundant making them an over-represented majority, while the data of some other
classes (e.g., the cancerous lesions) is deficient, making them an
underrepresented minority. It is more crucial to precisely identify the samples
from an underrepresented (i.e., in terms of the amount of data) but more
important minority class (e.g., certain cancerous lesion). In other words,
misclassifying a more severe lesion to a benign or less severe lesion should
have relative more cost (e.g., money, time and even lives). To address such
challenges, we present CS-AF, a cost-sensitive multi-classifier active fusion
framework for skin lesion classification. In the experimental evaluation, we
prepared 96 base classifiers (of 12 CNN architectures) on the ISIC research
datasets. Our experimental results show that our framework consistently
outperforms the static fusion competitors.
