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. 2023 Jun 23;3:91. doi: 10.1038/s43856-023-00312-x

Fig. 4. Classification accuracies for four new COVID-19 LFA kits with different numbers of training images used and with ablated models.

Fig. 4

Ablation studies were carried out to analyze the relative contributions of self-supervised learning for feature extraction and supervised contrastive learning for adaptation. Each model was evaluated by varying the number of images used in the adaptation. Accuracy scores reported for four new assay kits, Flowflex (a), DeepBlue (b), Jinwofu (c), and ACON IgG/IgM (d). (The EcoTest housing 2 kit was identical in all aspects to the base kit expect for the housing, so the direct application of the base model without any adaptation was able to achieve 100% kit-level accuracies). The maximum accuracy indicates the upper bound attained by training a model from scratch using all training images for each kit.