Partly Supervised Multi-Task Learning

Semi-supervised learning has recently been attracting attention as an alternative to fully supervised models that require large pools of labeled data. Moreover, optimizing a model for multiple tasks can provide better generalizability than single-task learning. Leveraging self-supervision and adversarial training, we propose a novel, general purpose semi-supervised, multiple-task model-namely, self-supervised, semi-supervised, multi-task learning (S
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MTL)-for accomplishing two important medical image analysis tasks: segmentation and diagnostic classification. Experimental results on chest and spine X-ray datasets confirm that our S
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MTL model significantly outperforms semi-supervised single-task, semi/fully-supervised multi-task, and fully-supervised single-task models, even with a 50% reduction in class and segmentation labels.