Multimix: Sparingly-Supervised, Extreme Multitask Learning from Medical Images

@article{Haque2021MultimixSE,
  title={Multimix: Sparingly-Supervised, Extreme Multitask Learning from Medical Images},
  author={Ayaan Haque and Abdullah-Al-Zubaer Imran and Adam S. Wang and Demetri Terzopoulos},
  journal={2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)},
  year={2021},
  pages={693-696}
}
Semi-supervised learning from limited quantities of labeled data, an alternative to fully-supervised schemes, benefits by maximizing knowledge gains from copious unlabeled data. Furthermore, learning multiple tasks within the same model improves model generalizability. We propose MultiMix, a novel multitask learning model that jointly learns disease classification and anatomical segmentation in a sparingly supervised manner, while preserving explainability through bridge saliency between the… 

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