Gradient-Aware Revitalization of Non-Effective Samples in Medical Image Segmentation

Deep learning-based medical image segmentation, with its precise lesion localization capabilities, serves as a core component in multimedia medical applications and intelligent diagnostic assistance systems. Recent innovations in network architectures significantly improve segmentation performance. However, the Non-Effective Samples (NES) on model optimization receive little attention. These samples are characterized by minimal gradient variations in loss during training and exhibit a slight contribution to model optimization. They encompass well-segmented samples with near-zero loss values and challenging samples with consistently high loss values. Especially, when NES accumulate, the model will fall into an optimization trap, causing the optimization to stagnate. To address this issue, we propose a lightweight plug-and-play Gradient-Aware Sample Selection and Reactivation Strategy (GA-SRS) that efficiently identifies and revitalizes the training potential of NES. Firstly, GA-SRS filters NES out based on the historical training information of samples and the variations in the loss gradient during the training process. Then, GA-SRS revitalizes the training values of these samples through strong data augmentation. Extensive experiments on four public datasets and three general models demonstrate the effectiveness of GA-SRS. For example, GA-SRS helps improve the IoU metric of U-KAN from 67.20% to 71.24% on the BUSI and from 81.20% to 82.51% on the ISIC dataset, achieving state-of-the-art experimental results.