Distilling OCT cervical dataset with evidential uncertainty proxy

Abstract

Deep learning-based OCT image classification method is of paramount importance for early screening of cervical cancer. For the sake of efficiency and privacy, the emerging data distillation technique becomes a promising way to condense the large-scale original OCT dataset into a much smaller synthetic dataset, without losing much information for network training. However, OCT cervical images often suffer from redundancy, mis-operation and noise, etc. These challenges make it hard to compress as much valuable information as possible into extremely small synthesized dataset. To this end, we design an uncertainty-aware distribution matching based dataset distillation framework (UDM). Precisely, we adopt a pre-trained plug-and-play uncertainty estimation proxy to compute classification uncertainty for each data point in the original and synthetic dataset. The estimated uncertainty allows us to adaptively calculate class-wise feature centers of the original and synthetic data, thereby increasing the importance of typical patterns and reducing the impact of redundancy, mis-operation, and noise, etc. Extensive experiments show that our UDM effectively improves distribution-matching-based dataset distillation under both homogeneous and heterogeneous training scenarios.