Fig. 3: Model performance under the centralized inference protocol. | npj Digital Medicine

Fig. 3: Model performance under the centralized inference protocol.

From: Diagnosing pathologic myopia by identifying morphologic patterns using ultra widefield images with deep learning

Fig. 3

a The proposed models are compared to four well-known benchmarks: DeiT, ConvNeXt, EfficientNet, and Swin Transformer. b The proposed models are compared to two recent foundation models: DINOv2 and VisionFM. The error bars represent the 95% confidence interval of the estimates, and the bar center represents the mean estimate of the displayed metric. The estimates are computed by generating a bootstrap distribution with 1000 bootstrap samples for corresponding testing sets with n = 1000 samples. All P-values are computed with a two-sided t-test between RealMNet-Max and the most competitive comparison model to determine if there are statistically significant differences.

Back to article page