Detecting and refurbishing ground truth errors during training of deep learning-based echocardiography segmentation models

arXiv:2604.12832v1 Announce Type: cross Abstract: Deep learning-based medical image segmentation typically relies on ground truth (GT) labels obtained through manual annotation, but these can be prone to random errors or systematic biases. This study examines the robustness of deep learning models to such errors in echocardiography (echo) segmentation and evaluates a novel strategy for detecting and refurbishing erroneous labels during model training. Using the CAMUS dataset, we simulate three e