Towards Sleep Scoring Generalization Through Self-Supervised Meta-Learning
This work addresses sleep scoring generalization for medical applications, but it is incremental as it builds on existing MAML and SSL frameworks.
The authors tackled the problem of sleep scoring generalization across patients and facilities by introducing S2MAML, a meta-learning method with self-supervised learning, which outperformed MAML and standard supervised learning on multiple datasets.
In this work we introduce a novel meta-learning method for sleep scoring based on self-supervised learning. Our approach aims at building models for sleep scoring that can generalize across different patients and recording facilities, but do not require a further adaptation step to the target data. Towards this goal, we build our method on top of the Model Agnostic Meta-Learning (MAML) framework by incorporating a self-supervised learning (SSL) stage, and call it S2MAML. We show that S2MAML can significantly outperform MAML. The gain in performance comes from the SSL stage, which we base on a general purpose pseudo-task that limits the overfitting to the subject-specific patterns present in the training dataset. We show that S2MAML outperforms standard supervised learning and MAML on the SC, ST, ISRUC, UCD and CAP datasets.