Bram Ruijsink

Marginal loss outperforms other loss functions in complex echocardiography segmentation tasks with multiple missing labels, revealing a new frontier in handling partially labelled data.

Even a standard U-Net can maintain surprisingly strong performance in echocardiography segmentation despite significant ground truth errors, but a Variance of Gradients approach can boost performance further by detecting and correcting those errors during training.