Partly Supervised Multitask Learning
This addresses annotation scarcity for joint segmentation and classification in medical imaging, with potential broader vision applications, though it appears incremental as it combines existing techniques like self-supervision and adversarial training.
The paper tackles the problem of limited labeled data in medical imaging by proposing a semi-supervised multitask learning model, which significantly outperforms baseline models even with a 50% reduction in labels on chest and spine X-ray datasets.
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, multitask learning (S$^4$MTL)---for accomplishing two important tasks in medical imaging, segmentation and diagnostic classification. Experimental results on chest and spine X-ray datasets suggest that our S$^4$MTL model significantly outperforms semi-supervised single task, semi/fully-supervised multitask, and fully-supervised single task models, even with a 50\% reduction of class and segmentation labels. We hypothesize that our proposed model can be effective in tackling limited annotation problems for joint training, not only in medical imaging domains, but also for general-purpose vision tasks.