Fig. 5: Effects of the number of generated images.
From: Improving AI models for rare thyroid cancer subtype by text guided diffusion models
a Comparative evaluation of the effect of downstream tasks based on different amplification ratios of different amplification methods in benign-malignant binary thyroid cancer prediction tasks. The results reveal performance limitations of other methods, possibly due to insufficient diversity in the detailed features of generated samples, while the Tiger Model shows substantial advantages by continuously improving performance. b Tiger data amplification and the addition of an equivalent amount of real image amplification were compared with the results of the downstream classification of rare subtypes. The FTC and MTC malignant tumor classification tasks showed that the model trained on the Tiger Model enhancement data was significantly better than the unamplified model, and the results were similar to the real image amplification. The AUC results are presented as mean values, with error bars representing 95% confidence intervals derived from n = 50 experimental replicates for each task setting. In each replicate trial, the basic real images (x) were selected through bootstrap sampling from the real image set. Source data are provided as a Source Data file.
