Contrastive learning with token projection for Omicron pneumonia identification from few-shot chest CT images - Jiang, Xiaoben; Yang, Dawei; Feng, Li; Zhu, Yu; Wang, Mingliang; Feng, Yinzhou; Bai, Chunxue; Fang, Hao (Frontiers in Medicine, 2024)
Introduction: Deep learning-based methods can promote and save critical time for pneumonia diagnosis from chest computed tomography (CT) images, while the methods usually rely on large amounts of labeled data to learn good visual representations. On the contrary, medical images are difficult to obtain and need to be labeled by professional radiologists. Methods: To address this issue, a novel contrastive learning model with token projection, namely CoTP, is proposed for improving few-shot chest CT images diagnostic quality. Specifically, (1) we utilize solely unlabeled data for fitting CoTP, along with a small number of labeled samples for fine-tuning, (2) we present a new Omicron dataset and modify the data augmentation strategy, i.e. random Poisson noise perturbation for the CT interpretation task, and (3) token projection is utilized to further improve the quality of the global visual representations. Results: The ResNet50 pre-trained by CoTP attained accuracy (ACC) of 92.35%, sensitivity (SEN) of 92.96%, precision (PRE) of 91.54%, and the area under the receiver-operating characteristics curve (AUC) of 98.90% on the presented Omicron dataset. On the contrary, the ResNet50 without pre-training achieved ACC, SEN, PRE, and AUC of 77.61%, 77.90%, 76.69%, and 85.66%, respectively. Conclusions: Extensive experiments reveal that a model pre-trained by CoTP greatly outperforms that without pre-training. The CoTP can improve the efficacy of diagnosis and reduce the heavy workload of radiologists for screening of Omicron pneumonia.