Training-Free Dataset Pruning for Polyp Segmentation via Community Detection in Similarity Networks

Abstract

Recent advances in deep learning have been driven by the availability of larger datasets and more complex models; however, this progress comes at the expense of substantial computational and annotation costs. To address these issues, we introduce a new, training-free dataset pruning method, *PRIME*, targeting polyp segmentation in medical imaging. To this end, *PRIME* constructs a similarity network among images in the target dataset and then applies community detection to retain a much smaller, yet representative subset of images from the original dataset. Unlike existing methods that require model training for dataset pruning, our *PRIME* completely avoids model training, thus significantly reducing computational demands. The reduction in the training dataset reduces 56.2% data annotation costs and enables 2.3$\times$ faster training of polyp segmentation models compared to training on the entire annotated dataset, with only a 0.5% drop in the DICE score. Consequently, our *PRIME* enables efficient training, fine-tuning, and domain adaptation across medical centers, thus offering a cost-effective solution for deep learning in polyp segmentation. Our implementation is available at https://github.com/SLDGroup/PRIME.