Fig. 5: PICTURE detected pathology manifestations not represented in the training set.
From: Uncertainty-aware ensemble of foundation models differentiates glioblastoma from its mimics
A The 2021 WHO Classification of Tumors of the Central Nervous System defines 109 types of CNS cancers, and most of these cancer types have an incidence rate lower than 0.1 per 100,000 person-years. We employed the uncertainty quantification capability of PICTURE to identify normal tissues and CNS tumor types (non-glioblastoma and non-PCNSL; diagnostic categories not included in the training dataset). B A PICTURE model trained to recognize glioblastoma and PCNSL successfully identified non-glioblastoma and non-PCNSL samples with an AUROC of 0.919, significantly outperforming existing OOD detection methods such as Monte Carlo dropout (AUROC = 0.666, P-value < 0.001) and deep ensemble (AUROC = 0.554, P-value < 0.001). P-values were determined by one-sided bootstrap significance tests (N = 1000). The shaded areas show the 95% confidence intervals estimated by 1000 bootstrap samples, and the solid lines represent the average sensitivity and specificity. C UMAP visualization of the image feature space in the test set showed that in-distribution glioblastoma (red isolines), PCNSL (blue isolines), and out-of-distribution (orange isolines) samples occupied distinct feature spaces. The color of the dots shows the epistemic certainty measurement of a given sample quantified by PICTURE, and the colored isolines show the kernel density distribution of each tumor type. Lighter dots represent samples with high levels of certainty. Samples represented by darker colors have lower certainty scores according to the PICTURE model, which was trained with in-distribution cases only. Samples with certainty scores lower than 0.5 are predominantly (84.7%) non-glioblastoma and non-PCNSL cases, while the remaining 15.3% consist of misidentified GBM (75.3%) and PCNSL (24.7%) cases.
