The recent state-of-the-art deep learning methods have significantly improved brain tumor segmentation. However, fully supervised training requires a large amount of manually labeled masks, which is highly time-
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Compared with learning only from the scribbles with partial Cross-Entropy on these two datasets, our method improved the average Dice by around 6.0 and 15.5 percentage points, respectively, and it significantly outperformed five state-of-the-art weakly supervised methods....
While supervised methods may lead to good performance, they require to fully annotate additional data which may not be an option in practice. In contrast, unsupervised methods don't need additional annotations but are usually unstable and hard to train. In this work, we propose a novel weakly-...
Our method outperforms other weakly annotated segmentation methods on the ACDC and ISIC2018 datasets, as shown by extensive experiments. The results show the segmentation performance of the proposed network is superiorly increased by approximately 1.8%, 3.5% and 1.6% on IoU , CPA and Dice , ...