Keywords: Diagnosis/Prediction, Segmentation, Meta-Learning, Gradient Similarity, Label Noise, Medical Image Segmentation, Brain Tumor Segmentation, BraTS-PEDs
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.
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