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Deep learning models have gained remarkable performance on a variety of imageclassification tasks. However, many models suffer from limited performance inclinical or medical settings when data are imbalanced. To address thischallenge, we propose a medical-knowledge-guided one-class classificationapproach that leverages domain-specific knowledge of classification tasks toboost the model's performance. The rationale behind our approach is that someexisting prior medical knowledge can be incorporated into data-driven deeplearning to facilitate model learning. We design a deep learning-basedone-class classification pipeline for imbalanced image classification, anddemonstrate in three use cases how we take advantage of medical knowledge ofeach specific classification task by generating additional middle classes toachieve higher classification performances. We evaluate our approach on threedifferent clinical image classification tasks (a total of 8459 images) and showsuperior model performance when compared to six state-of-the-art methods. Allcodes of this work will be publicly available upon acceptance of the paper.

Medical Knowledge-Guided Deep Learning for Imbalanced Medical Image Classification