2021/11/20 by Long Gao, Chang Liu, Gao, Long +9
Computer Science · Medicine · #COVID-19 diagnosis using AI #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Imbalanced Data Classification Techniques #Machine Learning in Healthcare #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2111.10620
openalex publication_date 2021/11/20 · openalex created_date 2021/12/06 · openalex updated_date 2026/07/28
Deep learning models have gained remarkable performance on a variety of image classification tasks. However, many models suffer from limited performance in clinical or medical settings when data are imbalanced. To address this challenge, we propose a medical-knowledge-guided one-class classification approach that leverages domain-specific knowledge of classification tasks to boost the model's performance. The rationale behind our approach is that some existing prior medical knowledge can be incorporated into data-driven deep learning to facilitate model learning. We design a deep learning-based one-class classification pipeline for imbalanced image classification, and demonstrate in three use cases how we take advantage of medical knowledge of each specific classification task by generating additional middle classes to achieve higher classification performances. We evaluate our approach on three different clinical image classification tasks (a total of 8459 images) and show superior model performance when compared to six state-of-the-art methods. All codes of this work will be publicly available upon acceptance of the paper.