Towards Trustworthy AI for Glioma Diagnosis: A Task-Aware Evaluation of Uncertainty Quantification

arXiv:2609.39429 · cs.CV, cs.AI · Submitted 2026-09-30 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-30

Updated: 2026-09-30

Code: https://github.com/ErasmusMC-NeuroOnco/PrognosAIs-UQ

Terminology

• AUC — 68× in this paper · explained in 2 episodes
• Aggregation — 46× in this paper · explained in 1 episode
• Epistemic Uncertainty — 42× in this paper · explained in 10 episodes
• Aleatoric Uncertainty — 27× in this paper · explained in 5 episodes
• Uncertainty Quantification — 27× in this paper · explained in 8 episodes
• Calibration — 23× in this paper · explained in 7 episodes
• Accuracy — 17× in this paper · explained in 1 episode
• Deep Learning — 17× in this paper
• Neural Network — 11× in this paper
• Uncertainty Decomposition — 9× in this paper · explained in 2 episodes
• Average Precision — 7× in this paper
• Monte Carlo — 5× in this paper
• Adam — 4× in this paper · explained in 2 episodes
• Convolutional Neural Network — 4× in this paper
• Generalization — 4× in this paper · explained in 13 episodes
• Interpretability — 4× in this paper · explained in 15 episodes
• Receiver Operating Characteristic Area Under the Curve — 4× in this paper
• Alignment — 3× in this paper · explained in 1 episode
• Bayesian Framework — 2× in this paper · explained in 3 episodes
• Bayesian Inference — 2× in this paper · explained in 5 episodes
• Dice Similarity Coefficient — 2× in this paper
• Expected Calibration Error — 2× in this paper
• Fidelity — 2× in this paper · explained in 2 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• MCD with Deep Ensembles — 2× in this paper
• Monte Carlo Deep Ensembles — 2× in this paper
• Monte Carlo dropout — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Unified Framework — 2× in this paper · explained in 5 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Conformal Prediction — 1× in this paper · explained in 5 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Convolutional Neural Networks (CNNs) — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Diagnostic Tools — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Erasmus Glioma Database — 1× in this paper
• Evidential Learning — 1× in this paper
• Generalizability — 1× in this paper · explained in 6 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Mutual Information — 1× in this paper
• Negative Log-Likelihood — 1× in this paper
• Redundancy — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Test-time augmentation (TTA) — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• U-net architecture — 1× in this paper · explained in 3 episodes
• World Health Organization — 1× in this paper

Sources

• Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
• A Call to Reflect on Evaluation Practices for Failure Detection in Image Classification
• ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentation
• Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding
• Adam: A Method for Stochastic Optimization
• Understanding Measures of Uncertainty for Adversarial Example Detection

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