{"total":280,"rows":[{"id":"2107.03342","title":"A Survey of Uncertainty in Deep Neural Networks","created":"2021-07-07","cited_by_count":680,"area":null},{"id":"2211.02701","title":"MONAI: An open-source framework for deep learning in healthcare","created":"2022-11-04","cited_by_count":448,"area":null},{"id":"2112.03145","title":"Diffusion Models for Implicit Image Segmentation Ensembles","created":"2021-12-06","cited_by_count":198,"area":"diffusion"},{"id":"2105.13381","title":"Recent advances and clinical applications of deep learning in medical image analysis","created":"2021-05-27","cited_by_count":104,"area":null},{"id":"2410.00847","title":"Uncertainty-aware Reward Model: Teaching Reward Models to Know What is Unknown","created":"2024-10-01","cited_by_count":74,"area":"alignment"},{"id":"2411.11919","title":"VL-Uncertainty: Detecting Hallucination in Large Vision-Language Model via Uncertainty Estimation","created":"2024-11-18","cited_by_count":73,"area":"mllm"},{"id":"2210.03736","title":"Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis","created":"2022-10-05","cited_by_count":63,"area":null},{"id":"2110.08762","title":"Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation","created":"2021-10-17","cited_by_count":61,"area":null},{"id":"2302.08119","title":"A Review of Uncertainty Estimation and its Application in Medical Imaging","created":"2023-02-16","cited_by_count":59,"area":null},{"id":"2211.12717","title":"Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks","created":"2022-11-23","cited_by_count":49,"area":null},{"id":"2101.02323","title":"Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentation","created":"2021-01-07","cited_by_count":45,"area":null},{"id":"2412.20892","title":"Rethinking Aleatoric and Epistemic Uncertainty","created":"2024-12-30","cited_by_count":42,"area":null},{"id":"2307.10236","title":"Look Before You Leap: An Exploratory Study of Uncertainty Measurement for Large Language Models","created":"2023-07-16","cited_by_count":41,"area":null},{"id":"2203.12362","title":"MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images","created":"2022-03-23","cited_by_count":39,"area":null},{"id":"2310.06873","title":"A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods","created":"2023-10-09","cited_by_count":39,"area":null},{"id":"2204.04516","title":"Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathology","created":"2022-04-09","cited_by_count":37,"area":null},{"id":"2103.17235","title":"FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation","created":"2021-03-31","cited_by_count":36,"area":null},{"id":"2306.05671","title":"Topology-Aware Uncertainty for Image Segmentation","created":"2023-06-09","cited_by_count":32,"area":null},{"id":"2212.10888","title":"A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability","created":"2022-12-21","cited_by_count":31,"area":null},{"id":"2310.00800","title":"GraphPatcher: Mitigating Degree Bias for Graph Neural Networks via Test-time Augmentation","created":"2023-10-01","cited_by_count":31,"area":null},{"id":"2112.02268","title":"Bridging Pre-trained Models and Downstream Tasks for Source Code Understanding","created":"2021-12-04","cited_by_count":28,"area":null},{"id":"2303.09870","title":"TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation","created":"2023-03-17","cited_by_count":28,"area":"distill"},{"id":"2401.08501","title":"ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentation","created":"2024-01-16","cited_by_count":25,"area":null},{"id":"2111.01968","title":"A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applications","created":"2021-11-03","cited_by_count":24,"area":null},{"id":"2207.06873","title":"BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural Networks","created":"2022-07-14","cited_by_count":23,"area":null},{"id":"2402.06892","title":"Understanding Test-Time Augmentation","created":"2024-02-10","cited_by_count":23,"area":null},{"id":"2103.00429","title":"Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models","created":"2021-02-28","cited_by_count":22,"area":null},{"id":"2308.12965","title":"POCO: 3D Pose and Shape Estimation with Confidence","created":"2023-08-24","cited_by_count":21,"area":null},{"id":"2210.12256","title":"Uncertainty Estimates of Predictions via a General Bias-Variance Decomposition","created":"2022-10-21","cited_by_count":20,"area":null},{"id":"2401.13650","title":"Tyche: Stochastic In-Context Learning for Medical Image Segmentation","created":"2024-01-24","cited_by_count":20,"area":null},{"id":"2107.09892","title":"Towards Lower-Dose PET using Physics-Based Uncertainty-Aware Multimodal Learning with Robustness to Out-of-Distribution Data","created":"2021-07-21","cited_by_count":18,"area":null},{"id":"2110.13947","title":"Collaborative Uncertainty in Multi-Agent Trajectory Forecasting","created":"2021-10-26","cited_by_count":18,"area":null},{"id":"2207.03450","title":"TFCNs: A CNN-Transformer Hybrid Network for Medical Image Segmentation","created":"2022-07-07","cited_by_count":18,"area":null},{"id":"2304.03981","title":"Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification","created":"2023-04-08","cited_by_count":18,"area":null},{"id":"2308.06777","title":"Shrinking Class Space for Enhanced Certainty in Semi-Supervised Learning","created":"2023-08-13","cited_by_count":17,"area":null},{"id":"2106.15542","title":"Uncertainty-Guided Progressive GANs for Medical Image Translation","created":"2021-06-29","cited_by_count":16,"area":null},{"id":"2110.12467","title":"Robustness via Uncertainty-aware Cycle Consistency","created":"2021-10-24","cited_by_count":16,"area":null},{"id":"2309.04109","title":"From Text to Mask: Localizing Entities Using the Attention of Text-to-Image Diffusion Models","created":"2023-09-08","cited_by_count":16,"area":"diffusion"},{"id":"2309.15533","title":"Uncertainty Quantification via Neural Posterior Principal Components","created":"2023-09-27","cited_by_count":16,"area":null},{"id":"2107.05630","title":"Challenges for machine learning in clinical translation of big data imaging studies","created":"2021-07-07","cited_by_count":15,"area":null},{"id":"2110.01604","title":"CertainNet: Sampling-free Uncertainty Estimation for Object Detection","created":"2021-10-04","cited_by_count":15,"area":null},{"id":"2110.03343","title":"Uncertainty-aware GAN with Adaptive Loss for Robust MRI Image Enhancement","created":"2021-10-07","cited_by_count":15,"area":null},{"id":"2109.14172","title":"Semi-Supervised Segmentation of Radiation-Induced Pulmonary Fibrosis from Lung CT Scans with Multi-Scale Guided Dense Attention","created":"2021-09-29","cited_by_count":14,"area":null},{"id":"2302.11084","title":"Test-Time Distribution Normalization for Contrastively Learned Vision-language Models","created":"2023-02-22","cited_by_count":14,"area":"mllm"},{"id":"2202.07550","title":"Label fusion and training methods for reliable representation of inter-rater uncertainty","created":"2022-02-15","cited_by_count":13,"area":null},{"id":"2202.08199","title":"Less is More: Surgical Phase Recognition from Timestamp Supervision","created":"2022-02-16","cited_by_count":13,"area":null},{"id":"2207.05195","title":"Collaborative Uncertainty Benefits Multi-Agent Multi-Modal Trajectory Forecasting","created":"2022-07-11","cited_by_count":13,"area":null},{"id":"2212.07328","title":"Modeling Multimodal Aleatoric Uncertainty in Segmentation with Mixture of Stochastic Experts","created":"2022-12-14","cited_by_count":13,"area":null},{"id":"2405.18435","title":"QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge","created":"2024-03-19","cited_by_count":13,"area":null},{"id":"2406.03323","title":"Comparative Benchmarking of Failure Detection Methods in Medical Image Segmentation: Unveiling the Role of Confidence Aggregation","created":"2024-06-05","cited_by_count":12,"area":null},{"id":"2309.02995","title":"Continual Evidential Deep Learning for Out-of-Distribution Detection","created":"2023-09-06","cited_by_count":11,"area":null},{"id":"2309.10852","title":"Using AI Uncertainty Quantification to Improve Human Decision-Making","created":"2023-09-19","cited_by_count":11,"area":null},{"id":"1904.08377","title":"Gaze Training by Modulated Dropout Improves Imitation Learning","created":"2019-04-17","cited_by_count":10,"area":"offlinerl"},{"id":"2107.13508","title":"Uncertainty-Aware Credit Card Fraud Detection Using Deep Learning","created":"2021-07-28","cited_by_count":10,"area":null},{"id":"2301.06624","title":"TAAL: Test-time Augmentation for Active Learning in Medical Image Segmentation","created":"2023-01-16","cited_by_count":10,"area":null},{"id":"2102.01897","title":"Automatic Segmentation of Organs-at-Risk from Head-and-Neck CT using Separable Convolutional Neural Network with Hard-Region-Weighted Loss","created":"2021-02-03","cited_by_count":9,"area":null},{"id":"2109.10702","title":"A Quantitative Comparison of Epistemic Uncertainty Maps Applied to Multi-Class Segmentation","created":"2021-09-22","cited_by_count":9,"area":null},{"id":"2112.09693","title":"Generalisation effects of predictive uncertainty estimation in deep learning for digital pathology","created":"2021-12-17","cited_by_count":9,"area":null},{"id":"2204.02450","title":"Federated Cross Learning for Medical Image Segmentation","created":"2022-04-05","cited_by_count":9,"area":null},{"id":"2204.06546","title":"Disentangling Uncertainty in Machine Translation Evaluation","created":"2022-04-13","cited_by_count":9,"area":null},{"id":"2307.01798","title":"Edge-aware Multi-task Network for Integrating Quantification Segmentation and Uncertainty Prediction of Liver Tumor on Multi-modality Non-contrast MRI","created":"2023-07-04","cited_by_count":9,"area":null},{"id":"2307.09827","title":"Online Continual Learning for Robust Indoor Object Recognition","created":"2023-07-19","cited_by_count":9,"area":null},{"id":"2310.03967","title":"Sub-token ViT Embedding via Stochastic Resonance Transformers","created":"2023-10-06","cited_by_count":9,"area":null},{"id":"2404.17302","title":"Part-Guided 3D RL for Sim2Real Articulated Object Manipulation","created":"2024-04-26","cited_by_count":9,"area":null},{"id":"2505.05163","title":"Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models","created":"2025-05-08","cited_by_count":9,"area":"mllm"},{"id":"2105.02197","title":"Impact of individual rater style on deep learning uncertainty in medical imaging segmentation","created":"2021-05-05","cited_by_count":8,"area":null},{"id":"2105.08590","title":"UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion Model with Ensemble Monte Carlo Dropout for COVID-19 Detection","created":"2021-05-18","cited_by_count":8,"area":null},{"id":"2206.13607","title":"Improved Text Classification via Test-Time Augmentation","created":"2022-06-27","cited_by_count":7,"area":null},{"id":"2210.15614","title":"Private and Reliable Neural Network Inference","created":"2022-10-27","cited_by_count":7,"area":null},{"id":"2304.05104","title":"Approaching Test Time Augmentation in the Context of Uncertainty Calibration for Deep Neural Networks","created":"2023-04-11","cited_by_count":7,"area":null},{"id":"2304.13476","title":"Effect of latent space distribution on the segmentation of images with multiple annotations","created":"2023-04-26","cited_by_count":7,"area":null},{"id":"2307.00398","title":"ProbVLM: Probabilistic Adapter for Frozen Vision-Language Models","created":"2023-07-01","cited_by_count":7,"area":"mllm"},{"id":"2308.15141","title":"Uncertainty Aware Training to Improve Deep Learning Model Calibration for Classification of Cardiac MR Images","created":"2023-08-29","cited_by_count":7,"area":null},{"id":"2401.14487","title":"Neighbor-Aware Calibration of Segmentation Networks with Penalty-Based Constraints","created":"2024-01-25","cited_by_count":7,"area":null},{"id":"2102.01586","title":"U-LanD: Uncertainty-Driven Video Landmark Detection","created":"2021-02-02","cited_by_count":6,"area":null},{"id":"2109.09533","title":"Modeling Annotation Uncertainty with Gaussian Heatmaps in Landmark Localization","created":"2021-09-20","cited_by_count":6,"area":null},{"id":"2206.14746","title":"Placenta Segmentation in Ultrasound Imaging: Addressing Sources of Uncertainty and Limited Field-of-View","created":"2022-06-29","cited_by_count":6,"area":null},{"id":"2209.11679","title":"Rethinking Missing Data: Aleatoric Uncertainty-Aware Recommendation","created":"2022-09-22","cited_by_count":6,"area":null},{"id":"2309.07403","title":"Flexible Visual Recognition by Evidential Modeling of Confusion and Ignorance","created":"2023-09-14","cited_by_count":6,"area":null},{"id":"2311.11096","title":"On the Out of Distribution Robustness of Foundation Models in Medical Image Segmentation","created":"2023-11-18","cited_by_count":6,"area":null},{"id":"2406.17608","title":"Test-time generative augmentation for medical image segmentation","created":"2024-06-25","cited_by_count":6,"area":null},{"id":"2411.16370","title":"A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation","created":"2024-11-25","cited_by_count":6,"area":null},{"id":"2504.03478","title":"Probabilistic Machine Learning for Noisy Labels in Earth Observation","created":"2025-04-04","cited_by_count":6,"area":null},{"id":"2509.01144","title":"MetaSSL: A General Heterogeneous Loss for Semi-Supervised Medical Image Segmentation","created":"2025-09-01","cited_by_count":6,"area":null},{"id":"2101.11254","title":"Automatic Segmentation of Gross Target Volume of Nasopharynx Cancer using Ensemble of Multiscale Deep Neural Networks with Spatial Attention","created":"2021-01-27","cited_by_count":5,"area":null},{"id":"2102.08021","title":"Uncertainty-based method for improving poorly labeled segmentation datasets","created":"2021-02-16","cited_by_count":5,"area":null},{"id":"2112.13054","title":"Generalized Wasserstein Dice Loss, Test-time Augmentation, and Transformers for the BraTS 2021 challenge","created":"2021-12-24","cited_by_count":5,"area":null},{"id":"2208.02870","title":"Improved post-hoc probability calibration for out-of-domain MRI segmentation","created":"2022-08-04","cited_by_count":5,"area":null},{"id":"2208.02895","title":"Automatic Segmentation of the Placenta in BOLD MRI Time Series","created":"2022-08-04","cited_by_count":5,"area":null},{"id":"2301.11551","title":"Harmonizing Flows: Unsupervised MR harmonization based on normalizing flows","created":"2023-01-27","cited_by_count":5,"area":null},{"id":"2302.11349","title":"Steerable Equivariant Representation Learning","created":"2023-02-22","cited_by_count":5,"area":null},{"id":"2306.08909","title":"Bridging the Gap between Decision and Logits in Decision-based Knowledge Distillation for Pre-trained Language Models","created":"2023-06-15","cited_by_count":5,"area":"distill"},{"id":"2307.15647","title":"Multi-layer Aggregation as a key to feature-based OOD detection","created":"2023-07-28","cited_by_count":5,"area":null},{"id":"2309.13132","title":"Understanding Calibration of Deep Neural Networks for Medical Image Classification","created":"2023-09-22","cited_by_count":5,"area":null},{"id":"2309.16831","title":"Propagation and Attribution of Uncertainty in Medical Imaging Pipelines","created":"2023-09-28","cited_by_count":5,"area":null},{"id":"2403.15836","title":"VLM-CPL: Consensus Pseudo Labels from Vision-Language Models for Annotation-Free Pathological Image Classification","created":"2024-03-23","cited_by_count":5,"area":"mllm"},{"id":"2406.17640","title":"BayTTA: Uncertainty-aware medical image classification with optimized test-time augmentation using Bayesian model averaging","created":"2024-06-25","cited_by_count":5,"area":null},{"id":"2407.02893","title":"An Uncertainty-guided Tiered Self-training Framework for Active Source-free Domain Adaptation in Prostate Segmentation","created":"2024-07-03","cited_by_count":5,"area":null},{"id":"2203.00238","title":"Uncertainty categories in medical image segmentation: a study of source-related diversity","created":"2022-03-01","cited_by_count":4,"area":null},{"id":"2209.08686","title":"Uncertainty Aware Multitask Pyramid Vision Transformer For UAV-Based Object Re-Identification","created":"2022-09-19","cited_by_count":4,"area":null}],"next_cursor":"100"}