Fig. 2: TITAN evaluation.
From: A multimodal whole-slide foundation model for pathology
a, Impact of pretraining data size on TITANV and baselines across four challenging subtyping tasks. TITANV is pretrained with 12.5%, 25%, 50% and 100% of Mass-340K. b, The average performance of the four tasks against the number of parameters. c, Linear probe evaluation of TITAN and baselines on morphological classification, molecular status and survival prediction tasks. The mean pooling baseline uses the same patch encoder as TITAN (CONCHv1.5). Multiclass tasks are evaluated with balanced accuracy, binary tasks with AUROC and survival tasks with the concordance index. For external cohorts (DHMC, CPTAC), the classifier is trained on the corresponding TCGA cohort. All error bars represent s.d. based on bootstrapping (n = 1,000) or k-fold evaluation (k = 5). d, Ablation for positional encoding, number of transformer layers and inclusion of vision-pretraining stage. The performance is averaged across the four subtyping tasks. e, Change in performance of slide encoders averaged across the four subtyping tasks for different learning paradigms. For mean pooling and ABMIL, the respective patch encoder for each framework is used. PRISM fine-tuning is not evaluated as the fine-tuning recipes are not provided. f, Linear probe few-shot performance using K shots, K ∈ {1, 2, 4, 8, 16}, comparing baselines and ABMIL with CONCHv1.5. For each setting, 50 runs were performed. The center of each box plot (horizontal line) represents the median, with whiskers extending to data points within 1.5× the interquartile range. Statistical significance was assessed by fitting generalized linear mixed-effects model and two-sided Wald z test on the fitted model. Significance shown with respect to TITAN. P values for nonsignificant results are shown. **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001. C, number of classes; Ft., fine-tune.
