Uncertainty Quantification

Also known as UQ

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44 papers in the last four weeks, up 110% on the four weeks before. 0.4% of all new papers.

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Uncertainty of Vision Medical Foundation Models
Aug 31, 2026Haoxu Huang, Narges RazavianMedical Imaging Foundation Models Uncertainty Quantification
Center for Data Science, New York University · Department of Radiology, NYU Grossman School of Medicine · Department of Population Health, NYU Grossman School of Medicine
A Unified Perspective on Conformal Prediction and Wasserstein Distributionally Robust Optimization for Uncertainty Quantification
Aug 30, 2026Kehan Long, Yiqi Zhao, Pol Mestres +3Distribution Shift Robustness Uncertainty Quantification
Contextual Robotics Institute, University of California San Diego, La Jolla, CA, USA · Thomas Lord Department of Computer Science, University of Southern California, Los Angeles, CA, USA · Department of Mechanical and Civil Engineering, California Institute of Technology, Pasadena, CA, USA +1
Across-Design Uncertainty in Short Pricing Panels: Inference and Identification
Aug 21, 2026Pedro Cadahia DelgadoCausal Effect Estimation Uncertainty Quantification
PhD, Universidad de Huelva, Spain
Reliability analysis for BraTS-GoAT segmentation: a controlled robustness study of deep-ensemble uncertainty
Aug 13, 2026Riya Deepak Shet, Le ZhangUncertainty Quantification Neural Network Robustness
Department of Cancer and Genomic Sciences, School of Medical Sciences, College of Medicine and Health, University of Birmingham, Birmingham, United Kingdom · Department of Electronic, Electrical and Systems Engineering, School of Engineering, College of Engineering and Physical Sciences, University of Birmingham, Birmingham, United Kingdom
Structure-preserving uncertainty quantification for GENERIC dynamics
Aug 12, 2026Zequn He, Celia ReinaDynamical Systems Uncertainty Quantification
Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, 19104, PA, United States of America
Evaluating and Calibrating Diffusion Model-derived Uncertainty for Quantitative MRI Mapping
Aug 12, 2026Shishuai Wang, Stefan Klein, Juan A. Hernandez-Tamames +1Uncertainty Quantification Selective Prediction
Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands · Department of Imaging Physics, TU Delft, Delft, The Netherlands
Forward and Inverse Virtual Metrology for Phototransistor Gain: A Hierarchical, Uncertainty-Aware Approach for Small Production Datasets
Aug 12, 2026Mahshid Amirabgir, Lorenza Ferrario, Paolo Conci +2Uncertainty Quantification Semiconductor Manufacturing
Micro Nano Facility (MNF), Center for Sensors and Devices, Fondazione Bruno Kessler (FBK), Via Sommarive 18, Povo (Trento), 38123, Italy. · Microfab Solutions, Via Sommarive 18, Povo (Trento), 38123, Italy. · Department of Civil and Environmental Engineering (DICA), Politecnico di Milano, Piazza Leonardo da Vinci 32, Milan, 20133, Italy. +1
A Local Sinkhorn Framework for Conditional Distribution Reconstruction of Multidimensional Random Fields
Aug 12, 2026Mingtao Xia, Qijing ShenUncertainty Quantification Conditional Distribution Estimation
School of Mathematics, University of Birmingham, Watson Building, Edgbaston, Birmingham, B15 2TT, United Kingdom · Department of Mathematics, University of Houston, 3551 Cullen Blvd, Houston, 77204, Texas, United States · Nuffield Department of Medicine, University of Oxford, United Kingdom, Old Road Campus, Oxford, OX3 7BN, United Kingdom
Uncertainty-Aware Deep Learning for Genomics Applications: Insights from an Empirical Study
Aug 11, 2026Sepideh Saran, Mahsa Ghanbari, Uwe OhlerBayesian Neural Networks Uncertainty Quantification
The Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Germany · Department of Electrical Engineering and Computer Science, Technical University of Berlin, Germany · Department of Biology, Humboldt University of Berlin, Germany +1
VIDS-Seg: Towards Reliable Uncertainty Quantification in Pediatric Cardiac Ultrasound Segmentation
Aug 11, 2026Paul Fischer, Ece OzkanUltrasound Image Segmentation Uncertainty Quantification
Department of Biomedical Engineering, University of Basel, Basel, Switzerland
Sensor-Informed Per-Point Covariance for Structured-Light 3D Imaging
Aug 11, 2026Sehoon Tak, Jae-Sang HyunFringe Projection Profilometry Uncertainty Quantification
Department of Mechanical Engineering, Yonsei University, Seoul 03722, South Korea
Whisper-Aware LLM: Self-Supervised Uncertainty Learning for Robust Whispered Speech Recognition
Aug 11, 2026Gaopeng Xu, Zhenyu Wang, Zheng Xue +2Uncertainty Quantification Automatic Speech Recognition
Qwen Business Unit of Alibaba, China
Structure-Preserving Uncertainty Propagation in First-Order Proof Search
Aug 10, 2026Tanel TammetProbabilistic Logic Logical Reasoning
Tallinn University of Technology
Two-Step MV-DeepONet: Probabilistic Operator Learning for Uncertainty Propagation Driven by Random Input Fields
Aug 10, 2026Yupei Nie, Lei Wang, Jiasen LiuUncertainty Quantification PDE Surrogate Modeling
School of Mathematics and Physics, North China Electric Power University, Beijing 102206, P.R. China · School of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, P.R. China
Dynamic Distribution-Aware Uncertainty Tracking in Vision-Language Representation Learning
Aug 10, 2026Ao Zhou, Zhiwei Jiang, Zifeng Cheng +4Uncertainty Quantification
State Key Laboratory of Novel Software Technology, Nanjing University
CUPA-T2*: Covariance-Aware Uncertainty Propagation and Alignment for T2* Mapping in Accelerated MRI
Aug 9, 2026Gideon N. L. Rouwendaal, Natascha Niessen, Hannah Eichhorn +3Uncertainty Quantification Heteroscedastic Regression
Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Germany · School of Computation, Information & Technology, Technical University of Munich, Germany · GE HealthCare, Munich, Germany +6
Estimating Uncertainty in Galaxy Morphology Classification
Aug 9, 2026Kai Cheng, Ruoqi Wang, Qiong LuoUncertainty Quantification Uncertainty Decomposition
The Hong Kong University of Science and Technology, Hong Kong, China · The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China
From Uncertainty to Failure Attribution: Self-Diagnosing Models for Failure Attribution under Distribution Shift
Aug 8, 2026Yiyao YangDistribution Shift Robustness Uncertainty Quantification
Columbia University
Conformal Coverage Guarantees for Any Video Temporal Grounder
Aug 7, 2026Aseel Mohamed, Rasul Khanbayov, Erchin Serpedin +1Temporal Video Grounding Uncertainty Quantification
Department of Electrical and Computer Engineering, Texas A&M University at Qatar, Doha, Qatar · College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar · Department of Computer and Electrical Engineering, Texas A&M University, College Station, TX, USA
Tracing sources of epistemic uncertainty in deep learning predictions: homo- and hetero-scedastic linearized estimators
Aug 7, 2026Pierre Nodet, Thomas GeorgeUncertainty Quantification Aleatoric Uncertainty
Orange Research, Châtillon, France
Online Conformal Prediction Beyond Feedback
Aug 7, 2026Joar Skalse, Edoardo Pona, Osvaldo Simeone +1Uncertainty Quantification Conformal Prediction
King’s College London · Northeastern University London
UQ-Loc: Uncertainty-Aware LiDAR Scene Coordinate Regression
Aug 6, 2026Jacek KomorowskiUncertainty Quantification LiDAR Localization
Warsaw University of Technology, Warsaw, Poland
A Ground-Truth Framework for Uncertainty Disentanglement with Posterior Risk
Aug 6, 2026Frieder Wizgall, Georg Tirpitz, Moritz Seiler +2Uncertainty Quantification Aleatoric Uncertainty
University of Tübingen, Tübingen, Germany · Hertie Institute for AI in Brain Health, University of Tübingen, Tübingen, Germany · Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany +2
Uncertainty-Aware World Model for Aerial Image-Goal Navigation
Aug 6, 2026Deyi Zhu, Haoyu Fan, Yinan Zhu +4Uncertainty Quantification World Models for Robotics
Tsinghua Shenzhen International Graduate School, Tsinghua University
Hybrid Probabilistic Zonotopes for Identifiable and Refinable Predictive Uncertainty
Aug 5, 2026Zhen Zhang, Amr AlanwarUncertainty Quantification Probabilistic Forecasting
School of Computation, Information and Technology, Technical University of Munich, Germany
Stochastic Emulation using Generalized Stratified Sampling for Performance-Based Risk Optimization of Structures
Aug 5, 2026Isabela D. Rodrigues, Seymour M. J. Spence, Henrique M. Kroetz +1Surrogate Modeling Uncertainty Quantification
Department of Structural Engineering, São Carlos School of Engineering, University of São Paulo, São Carlos, 13566-590, São Paulo, Brazil · Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, 48109, Michigan, USA · Center for Marine Studies, Federal University of Paraná, Pontal do Paraná, 13566-590, Paraná, Brazil
Quality Diversity for Reliable Data Driven Time-Use Optimization
Aug 5, 2026Aneta Neumann, Ty Stanford, Dorothea Dumuid +1Uncertainty Quantification Quality-Diversity Optimization
Optimisation and Logistics School of Computer and Mathematical Sciences Adelaide University Adelaide, Australia · School of Allied Health and Human Performance Adelaide University Adelaide, Australia
Real-time probabilistic tsunami forecasting via generative AI
Aug 5, 2026Yusuke Oishi, Takashi Furumura, Fumihiko ImamuraUncertainty Quantification Probabilistic Forecasting
Fujitsu Research, Fujitsu Limited, Kawasaki, Japan. · Earthquake Research Institute, The University of Tokyo, Tokyo, Japan. · International Research Institute of Disaster Science, Tohoku University, Sendai, Japan.
UHP Detection: LVLMs have their Unique Hallucination Pattern in the Consistency Space
Aug 4, 2026Amir Mohammad Ezzati, Kiyan Rezaee, Bardiya Kariminia +4Hallucination Detection in VLMs Vision-Language Models
Sharif University of Technology · Michigan State University · Shahid Beheshti University +2
Adaptive Modality Reliability Diagnosis and Restoration for Robust Multimodal Intent Recognition
Aug 4, 2026Suraj Kumar, Mohnish Raj, Soumi Chattopadhayay +2Missing-Modality Learning Uncertainty Quantification

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Uncertainty of Vision Medical Foundation Models

Haoxu Huang, Narges Razavian

Center for Data Science, New York University · Department of Radiology, NYU Grossman School of Medicine · Department of Population Health, NYU Grossman School of Medicine

Medical Imaging Foundation Models Uncertainty Quantification Conformal Prediction Uncertainty Calibration

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Accurate uncertainty estimation is essential for machine learning systems de- ployed in high-stakes domains such as medicine. Traditional approaches primarily rely on probability outputs from trained models (point predictions), which provide no formal guarantees on prediction coverage and often require additional calibra- tion techniques to improve reliability. In contrast, conformal prediction (region prediction) offers a principled alternative by generating prediction sets with finite- sample validity guarantees, ensuring that the ground truth is contained within the set at a specified confidence level. In this study, we explore the impact of pre-training approach, dataset scale and domain on both point and region-level uncertainty quantification, by studying domain-specific vision medical foundation models vs. general domain vision foundation models. We conduct a comprehensive evaluation across foundation models trained on retinal, histopathological, and Chest X-Rays data, applying various calibration techniques. Our results demonstrate that (1) pre-training on higher-quality domain-specific datasets along with self-supervised learning leads to better-calibrated point predictions than general domain pre-training, (2) stan- dard re-calibration methods alone cannot fully mitigate uncertainty discrepancies across models trained on different data sources, (3) domain-specific foundation model can lead to more efficient conformal prediction. These findings highlight the importance of careful model selection and the inte- gration of both point and region prediction to enhance the reliability and trust- worthiness of medical AI systems. Our work underscores the need for a holistic approach to uncertainty quantification in recent development of medical vision foundation model, ensuring robust and interpretable AI-driven decision-making.

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