Uncertainty-aware Condensation for OCT Cervical Dataset Combining Coreset Selection and Distillation

Optical Coherence Tomography (OCT) cervical images offer a sensitive and non-invasive solution for screening cervical cancer. Currently, utilizing deep learning techniques to interpret these images has emerged as a promising approach for computer-aided diagnosis. However, challenges in computational efficiency and privacy protection persist in this field. To address these issues, we propose an uncertainty-aware filtering before distillation (UFD) framework to compress the OCT cervical dataset in a trustworthy and efficient manner. Our approach involves pre-training an uncertainty proxy to evaluate the importance of each sample, allowing us to extract a valuable subset (i.e., coreset) from the original dataset. We then apply importance-weighted distribution matching for dataset distillation. The distribution matching loss is calculated as the distance between the class centers of the uncertainty-weighted coreset and the synthetic distilled dataset. Extensive experiments validate the feasibility and effectiveness of our proposed UFD framework. It is worth noting that after selecting the most representative samples from the original OCT dataset through coreset selection, the performance of the trained network is comparable to, or sometimes even slightly higher than, that of the network trained with all the original data. This indicates that automated dataset compression and cleaning techniques are crucial for improving image classification problems in medical imaging datasets, such as OCT and ultrasound, which are rich in noise and erroneous samples.