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Rapid on-site cytologic evaluation (ROSE) for samples taken during endobronchial ultrasound guided transbronchial needle aspiration (EBUS-TBNA) is effective to improve the diagnostic yield and to avoid repeated puncture procedures. However, ROSE is not widely used due to limited human resources, especially limited availability of staff who performs benign and malignant classification. Thus, we developed an artificial intelligence system to assist in ROSE diagnoses. We had used in-house dataset consists of about 750 cells cropped from 26 pathological slides labeled by a pulmonologist and cytotechnologist for training and testing. Since false positive should be minimized as much as possible to prevent a repeat bronchoscopy, specificity should be maximized while maintaining acceptable sensitivity. Thus, we introduced a critical index Spec@sens0.9, which means the specificity when sensitivity is 0.9. We compared the following three methods, 1. Conventional learning without contrastive learning-based pretraining, 2. General contrastive learning with positive and negative samples like SimCLR, and 3. Proposal method that is a contrastive learning with positive, hard negative and easy negative samples based on distance metric in embedding spaces. Spec@sens0.9 by the three methods were found to be 0.879/0.935/0.944, respectively. Since the dataset in this study consists of limited number of labels, we thought that the pretraining by contrastive learning, that should work as a self-supervised data augmentation, was effective to improve the model’s performance. Our proposal method was effective to enhance the model’s performance compared with the general contrastive learning.
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