For image segmentation, typical fully convolutional networks (FCNs) need strong supervision through a large sample of high-quality dense segmentations, entailing high costs in expert-raters’ time and effort. We propose MS-Net, a new FCN to significantly reduce supervision cost, and improve performance, by coupling strong supervision with weak supervision through low-cost input in the form of bounding boxes and landmarks. Our MS-Net enables instance-level segmentation at high spatial resolution, with feature extraction using dilated convolutions. We propose a new loss function using bootstrapped Dice overlap for precise segmentation. Results on large datasets show that MS-Net segments more accurately at reduced supervision costs, compared to the state of the art.
CITATION STYLE
Shah, M. P., Merchant, S. N., & Awate, S. P. (2018). MS-Net: Mixed-Supervision Fully-Convolutional Networks for Full-Resolution Segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11073 LNCS, pp. 379–387). Springer Verlag. https://doi.org/10.1007/978-3-030-00937-3_44
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