Weakly Supervised Medical Image Segmentation with Soft Labels and Noise Robust Loss

Acquiring large expert labeled annotation for medical images is expensive and many of the available datasets contain noisy labels. Training deep learning models with incorrect and noisy labels may introduce bias to the system, which could lead to false diagnoses in medical applications. Therefore there is a need for models that are robust against the label noise. In this study, we proposed to use soft labels in addition to adapted noise robust loss to learn from weak labels. Our experiments shows the proposed method is effective for highly noisy segmentation labels.