An Attentive Representative Sample Selection Strategy Combined with Balanced Batch Training for Skin Lesion Segmentation
Journal:
arXiv
Published Date:
Mar 21, 2025
Abstract
An often overlooked problem in medical image segmentation research is the
effective selection of training subsets to annotate from a complete set of
unlabelled data. Many studies select their training sets at random, which may
lead to suboptimal model performance, especially in the minimal supervision
setting where each training image has a profound effect on performance
outcomes. This work aims to address this issue. We use prototypical contrasting
learning and clustering to extract representative and diverse samples for
annotation. We improve upon prior works with a bespoke cluster-based image
selection process. Additionally, we introduce the concept of unsupervised
balanced batch dataloading to medical image segmentation, which aims to improve
model learning with minimally annotated data. We evaluated our method on a
public skin lesion dataset (ISIC 2018) and compared it to another
state-of-the-art data sampling method. Our method achieved superior performance
in a low annotation budget scenario.
