To tackle the problem of limited annotated data, semisupervised learning is attracting attention as an alternative to fully supervised models. Moreover, optimizing a multipletask model to learn "multiple contexts" can provide better generalizability compared to single-task models. We propose a novel semi-supervised multiple-task model leveraging selfsupervision 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 semisupervised single-task, semi-supervised multi-context, and fully-supervised single-task models, even with a 50% reduction of classification and segmentation labels.
|Title of host publication||AAAI 2020 - 34th AAAI Conference on Artificial Intelligence|
|Number of pages||2|
|State||Published - 2020|
|Event||34th AAAI Conference on Artificial Intelligence, AAAI 2020 - New York, United States|
Duration: Feb 7 2020 → Feb 12 2020
|Name||AAAI 2020 - 34th AAAI Conference on Artificial Intelligence|
|Conference||34th AAAI Conference on Artificial Intelligence, AAAI 2020|
|Period||2/7/20 → 2/12/20|
Bibliographical notePublisher Copyright:
© 2020 The Twenty-Fifth AAAI/SIGAI Doctoral Consortium (AAAI-20). All Rights Reserved.
ASJC Scopus subject areas
- Artificial Intelligence