Self-Supervised, Semi-Supervised, Multi-Context Learning for the Combined Classification and Segmentation of Medical Images (Student Abstract)

2020-06-02

Proceedings of the AAAI Conference on Artificial Intelligence, 34

Volume

Vol. 34 No. 10: Issue 10: AAAI-20 Student Tracks

Student Abstract Track

To tackle the problem of limited annotated data, semi-supervised learning is attracting attention as an alternative to fully supervised models. Moreover, optimizing a multiple-task model to learn “multiple contexts” can provide better generalizability compared to single-task models. We propose a novel semi-supervised multiple-task model leveraging self-supervision and adversarial training—namely, self-supervised, semi-supervised, multi-context learning (S4MCL)—and apply it to two crucial medical imaging tasks, classification and segmentation. Our experiments on spine X-rays reveal that the S4MCL model significantly outperforms semi-supervised single-task, semi-supervised multi-context, and fully-supervised single-task models, even with a 50% reduction of classification and segmentation labels.

10.1609/aaai.v34i10.7179