Existing deep learning models have achieved promising performance in
recognizing skin diseases from dermoscopic images. However, these models can
only recognize samples from predefined categories, when they are deployed in
the clinic, data from new unknown categories are constantly emerging.
Therefore, it is crucial to automatically discover and identify new semantic
categories from new data. In this paper, we propose a new novel class discovery
framework for automatically discovering new semantic classes from dermoscopy
image datasets based on the knowledge of known classes. Specifically, we first
use contrastive learning to learn a robust and unbiased feature representation
based on all data from known and unknown categories. We then propose an
uncertainty-aware multi-view cross pseudo-supervision strategy, which is
trained jointly on all categories of data using pseudo labels generated by a
self-labeling strategy. Finally, we further refine the pseudo label by
aggregating neighborhood information through local sample similarity to improve
the clustering performance of the model for unknown categories. We conducted
extensive experiments on the dermatology dataset ISIC 2019, and the
experimental results show that our approach can effectively leverage knowledge
from known categories to discover new semantic categories. We also further
validated the effectiveness of the different modules through extensive ablation
experiments. Our code will be released soon.

提出一种基于已知类别知识自动发现皮肤镜图像数据集中新语义类别的新颖类别发现框架，借助对已知类别知识的学习，采用对比学习和多视角交叉伪监督策略来建立特征表示，并通过众包和本地样本相似性聚合来改进模型的聚类性能。在 ISIC 2019 皮肤科数据集上进行广泛实验，结果表明，该方法能够有效利用已知类别知识发现新的语义类别。