About the Project
Despite recent advances, deep learning methods for medical image segmentation can be hampered by limited availability of expertly labeled data. This project proposes a novel approach to enhance segmentation quality by incorporating constraints on invariance, topology, dimensions, position, count, shape, and other segmentation properties. The method will also handle weak annotations like scribbles and global properties such as unbiasedness.
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