Self-supervised Multi-scale Consistency for Weakly Supervised Segmentation Learning

@inproceedings{Valvano2021SelfsupervisedMC,
  title={Self-supervised Multi-scale Consistency for Weakly Supervised Segmentation Learning},
  author={Gabriele Valvano and Andrea Leo and Sotirios A. Tsaftaris},
  booktitle={DART/FAIR@MICCAI},
  year={2021}
}
Collecting large-scale medical datasets with fine-grained annotations is time-consuming and requires experts. For this reason, weakly supervised learning aims at optimising machine learning models using weaker forms of annotations, such as scribbles, which are easier and faster to collect. Unfortunately, training with weak labels is challenging and needs regularisation. Herein, we introduce a novel self-supervised multiscale consistency loss, which, coupled with an attention mechanism… Expand

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