  Deep neural networks have demonstrated promising performance on image
recognition tasks. However, they may heavily rely on confounding factors, using
irrelevant artifacts or bias within the dataset as the cue to improve
performance. When a model performs decision-making based on these spurious
correlations, it can become untrustable and lead to catastrophic outcomes when
deployed in the real-world scene. In this paper, we explore and try to solve
this problem in the context of skin cancer diagnosis. We introduce a
human-in-the-loop framework in the model training process such that users can
observe and correct the model's decision logic when confounding behaviors
happen. Specifically, our method can automatically discover confounding factors
by analyzing the co-occurrence behavior of the samples. It is capable of
learning confounding concepts using easily obtained concept exemplars. By
mapping the black-box model's feature representation onto an explainable
concept space, human users can interpret the concept and intervene via first
order-logic instruction. We systematically evaluate our method on our newly
crafted, well-controlled skin lesion dataset and several public skin lesion
datasets. Experiments show that our method can effectively detect and remove
confounding factors from datasets without any prior knowledge about the
category distribution and does not require fully annotated concept labels. We
also show that our method enables the model to focus on clinical-related
concepts, improving the model's performance and trustworthiness during model
inference.
