Meta-Learning for Robust Medical Image Segmentation: A Gradient-Similarity Reweighting Approach to Mitigate Noisy Labels

Motivation: Automated and robust segmentation of medical image requires many data and clean labels. Goal(s): Our goal was to develop a reweighting approach to mitigate noisy labels via gradient similarity. Approach: We propose a novel meta-learning approach that dynamically reweights training samples based on their reliability by measuring cosine similarity between per-sample gradients of noisy data and clean meta-data, optimizing loss reweighting. Results: Comparative analysis showed that the proposed method improved glioblastoma segmentation significantly, with overall Dice scores (DSC) increasing from 0.768% to 0.792%. Notably, in the pediatric task, our method significantly improves segmentation performance by 2.7% in DSC. Impact: This advancement addresses noisy data and limited data availability in medical image segmentation, enabling more accurate and reliable predictions.