  Deep neural network-based medical image classifications often use "hard"
labels for training, where the probability of the correct category is 1 and
those of others are 0. However, these hard targets can drive the networks
over-confident about their predictions and prone to overfit the training data,
affecting model generalization and adaption. Studies have shown that label
smoothing and softening can improve classification performance. Nevertheless,
existing approaches are either non-data-driven or limited in applicability. In
this paper, we present the Class-Correlation Learning Network (CCL-Net) to
learn interclass visual correlations from given training data, and produce soft
labels to help with classification tasks. Instead of letting the network
directly learn the desired correlations, we propose to learn them implicitly
via distance metric learning of class-specific embeddings with a lightweight
plugin CCL block. An intuitive loss based on a geometrical explanation of
correlation is designed for bolstering learning of the interclass correlations.
We further present end-to-end training of the proposed CCL block as a plugin
head together with the classification backbone while generating soft labels on
the fly. Our experimental results on the International Skin Imaging
Collaboration 2018 dataset demonstrate effective learning of the interclass
correlations from training data, as well as consistent improvements in
performance upon several widely used modern network structures with the CCL
block.
