Key result
A contrastive predictive coding-based self-supervised learning framework (TCC) was designed for medical sensor data classification and compared with state-of-the-art supervised learning methods.
Why the study?
Fully supervised learning for medical data classification requires substantial manually annotated data consuming manpower and resources, which self-supervised learning can address.
A novel self-supervised learning framework based on contrastive predictive coding (TCC) is proposed for efficient diagnosis and classification of medical sensor data, reducing the need for large manually annotated datasets.
May reduce labeling needs for sensor classification; leaves open prospective clinical validation before adoption.
Supervised learning technologies have been used in medical-data classification to improve diagnosis efficiency and reduce human diagnosis errors. A large amount of manually annotated data are required for the fully supervised learning process. However, annotating data information will consume a large amount of manpower and resources. Self-supervised learning has great advantages in solving this problem. Self-supervised learning mainly uses pretext tasks to mine its own supervised information from large-scale unsupervised data. And this constructed supervised information is used to train the network to learn valuable representations for downstream tasks. This study designs a general and efficient model for the diagnosis and classification of medical sensor data based on contrastive predictive coding (CPC) in self-supervised learning, called TCC, which consists of two steps. The first step is to design a pretext task based on the idea of CPC, which aims to extract effective features between different categories using its encoder. The second step designs a downstream classification task with lower time and space complexity to perform a supervised type of training using the features extracted by the encoder of the pretext task. Finally, to demonstrate the performance of the proposed framework in this paper, we compare the proposed framework with recent state-of-the-art works. Experiments comparing the proposed framework with supervised learning are also set up under the condition of different proportions of labeled data.
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Ren et al. (2022) studied Medical sensor data classification. TCC (contrastive predictive coding-based classification framework) vs. State-of-the-art works and supervised learning was evaluated on Classification performance. A contrastive predictive coding-based self-supervised learning framework (TCC) was designed for medical sensor data classification and compared with state-of-the-art supervised learning methods.
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