An improved contrastive learning loss function for automated clock-drawing test grading with implications for cognitive impairment screening

Contrastive learning has been attracting much interest in recent years for its ability to train without labeled data. An important factor in its success is the loss function, which guides the search for prominent features that separate the positive and negative classes. The triplet loss function is widely used in contrastive learning, in which the objective is to attract a pair of positive instances while pushing away a negative instance from the anchor instance, where one of the positive instances is often an augmented version of the anchor. To improve the performance of contrastive learning in automated Clock-Drawing Test (CDT) grading, this paper proposes a more comprehensive triplet loss function that aims to not only keep the distance between the anchor and a positive instance small and the distance between the anchor and a negative instance large, but also keep the distance between the positive and negative instances large. Experimental results show that the improved loss function significantly improves the model’s accuracy, precision, recall, and F1-score by 3–5% on both CIFAR-10 and CDT datasets, providing a new method for improving the accuracy of automatic CDT scoring and early detection of cognitive impairments.