{"total":119,"rows":[{"id":"2110.06465","title":"Breaking the Dilemma of Medical Image-to-image Translation","created":"2021-10-13","cited_by_count":82,"area":null},{"id":"2108.04063","title":"Co-learning: Learning from Noisy Labels with Self-supervision","created":"2021-08-05","cited_by_count":51,"area":null},{"id":"2110.14222","title":"Sample Selection for Fair and Robust Training","created":"2021-10-27","cited_by_count":48,"area":null},{"id":"2109.00574","title":"Active label cleaning for improved dataset quality under resource constraints","created":"2021-09-01","cited_by_count":44,"area":null},{"id":"2303.06930","title":"Twin Contrastive Learning with Noisy Labels","created":"2023-03-13","cited_by_count":28,"area":null},{"id":"2203.14817","title":"Sketching without Worrying: Noise-Tolerant Sketch-Based Image Retrieval","created":"2022-03-28","cited_by_count":27,"area":null},{"id":"2107.04537","title":"Modality specific U-Net variants for biomedical image segmentation: A survey","created":"2021-07-09","cited_by_count":20,"area":null},{"id":"2111.14932","title":"Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection","created":"2021-11-29","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":"2308.12064","title":"SILT: Shadow-aware Iterative Label Tuning for Learning to Detect Shadows from Noisy Labels","created":"2023-08-23","cited_by_count":19,"area":null},{"id":"2307.11397","title":"Probabilistic Modeling of Inter- and Intra-observer Variability in Medical Image Segmentation","created":"2023-07-21","cited_by_count":17,"area":null},{"id":"2203.07788","title":"Scalable Penalized Regression for Noise Detection in Learning with Noisy Labels","created":"2022-03-15","cited_by_count":15,"area":null},{"id":"2303.09117","title":"Cross-Modal Causal Intervention for Medical Report Generation","created":"2023-03-16","cited_by_count":15,"area":null},{"id":"2103.00528","title":"Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation","created":"2021-02-28","cited_by_count":13,"area":null},{"id":"2203.10611","title":"Learning from Multiple Expert Annotators for Enhancing Anomaly Detection in Medical Image Analysis","created":"2022-03-20","cited_by_count":13,"area":null},{"id":"2312.12263","title":"FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labels","created":"2023-12-19","cited_by_count":13,"area":null},{"id":"2601.09065","title":"Beyond Consensus: Perspectivist Modeling and Evaluation of Annotator Disagreement in NLP","created":"2026-01-14","cited_by_count":13,"area":null},{"id":"2111.04239","title":"Learning to Rectify for Robust Learning with Noisy Labels","created":"2021-11-08","cited_by_count":12,"area":null},{"id":"2111.13410","title":"Modeling Annotator Preference and Stochastic Annotation Error for Medical Image Segmentation","created":"2021-11-26","cited_by_count":12,"area":null},{"id":"2208.09779","title":"Robust Node Classification on Graphs: Jointly from Bayesian Label Transition and Topology-based Label Propagation","created":"2022-08-21","cited_by_count":12,"area":null},{"id":"2306.03288","title":"Deep Learning From Crowdsourced Labels: Coupled Cross-entropy Minimization, Identifiability, and Regularization","created":"2023-06-05","cited_by_count":12,"area":null},{"id":"2208.03295","title":"Learning from data in the mixed adversarial non-adversarial case: Finding the helpers and ignoring the trolls","created":"2022-08-05","cited_by_count":11,"area":null},{"id":"2305.14258","title":"Weakly Supervised AUC Optimization: A Unified Partial AUC Approach","created":"2023-05-23","cited_by_count":11,"area":null},{"id":"2306.03116","title":"Transferring Annotator- and Instance-dependent Transition Matrix for Learning from Crowds","created":"2023-06-05","cited_by_count":11,"area":null},{"id":"2110.08355","title":"Clean or Annotate: How to Spend a Limited Data Collection Budget","created":"2021-10-15","cited_by_count":10,"area":null},{"id":"2207.11327","title":"Learning from Multiple Annotator Noisy Labels via Sample-wise Label Fusion","created":"2022-07-22","cited_by_count":10,"area":null},{"id":"2403.13417","title":"Diversified and Personalized Multi-rater Medical Image Segmentation","created":"2024-03-20","cited_by_count":9,"area":null},{"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":"2112.01197","title":"Sample Prior Guided Robust Model Learning to Suppress Noisy Labels","created":"2021-12-02","cited_by_count":8,"area":null},{"id":"2206.05092","title":"Learning self-calibrated optic disc and cup segmentation from multi-rater annotations","created":"2022-06-10","cited_by_count":8,"area":null},{"id":"2304.02539","title":"Multi-annotator Deep Learning: A Probabilistic Framework for Classification","created":"2023-04-05","cited_by_count":8,"area":null},{"id":"2305.04203","title":"Unlocking the Power of Open Set : A New Perspective for Open-Set Noisy Label Learning","created":"2023-05-07","cited_by_count":8,"area":null},{"id":"2403.09975","title":"Skeleton-Based Human Action Recognition with Noisy Labels","created":"2024-03-15","cited_by_count":8,"area":null},{"id":"2404.04159","title":"Noisy Label Processing for Classification: A Survey","created":"2024-04-05","cited_by_count":8,"area":null},{"id":"2201.10836","title":"PARS: Pseudo-Label Aware Robust Sample Selection for Learning with Noisy Labels","created":"2022-01-26","cited_by_count":7,"area":null},{"id":"2209.04360","title":"A Semi-Supervised Algorithm for Improving the Consistency of Crowdsourced Datasets: The COVID-19 Case Study on Respiratory Disorder Classification","created":"2022-09-09","cited_by_count":7,"area":null},{"id":"2302.02944","title":"Learning Complementary Policies for Human-AI Teams","created":"2023-02-06","cited_by_count":7,"area":null},{"id":"2307.02191","title":"Evaluating AI systems under uncertain ground truth: a case study in dermatology","created":"2023-07-05","cited_by_count":7,"area":null},{"id":"2402.02249","title":"Don't Label Twice: Quantity Beats Quality when Comparing Binary Classifiers on a Budget","created":"2024-02-03","cited_by_count":7,"area":null},{"id":"2107.10449","title":"Improve Learning from Crowds via Generative Augmentation","created":"2021-07-22","cited_by_count":6,"area":null},{"id":"2109.04470","title":"Truth Discovery in Sequence Labels from Crowds","created":"2021-09-09","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":"2409.09412","title":"Label Convergence: Defining an Upper Performance Bound in Object Recognition through Contradictory Annotations","created":"2024-09-14","cited_by_count":6,"area":null},{"id":"2411.17113","title":"Learning from Noisy Labels via Conditional Distributionally Robust Optimization","created":"2024-11-26","cited_by_count":6,"area":null},{"id":"2412.18342","title":"Mitigating Label Noise using Prompt-Based Hyperbolic Meta-Learning in Open-Set Domain Generalization","created":"2024-12-24","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":"2108.08212","title":"Confidence Adaptive Regularization for Deep Learning with Noisy Labels","created":"2021-08-18","cited_by_count":5,"area":null},{"id":"2204.13456","title":"Learning from Pixel-Level Noisy Label : A New Perspective for Light Field Saliency Detection","created":"2022-04-28","cited_by_count":5,"area":null},{"id":"2311.13172","title":"Learning to Complement with Multiple Humans","created":"2023-11-22","cited_by_count":5,"area":null},{"id":"2401.01650","title":"De-Confusing Pseudo-Labels in Source-Free Domain Adaptation","created":"2024-01-03","cited_by_count":5,"area":null},{"id":"2402.04398","title":"Learning under Temporal Label Noise","created":"2024-02-06","cited_by_count":5,"area":null},{"id":"2105.14083","title":"Rethinking Noisy Label Models: Labeler-Dependent Noise with Adversarial Awareness","created":"2021-05-28","cited_by_count":4,"area":null},{"id":"2106.15146","title":"Learning from Multiple Annotators by Incorporating Instance Features","created":"2021-06-29","cited_by_count":4,"area":null},{"id":"2405.08794","title":"Ambiguous Annotations: When is a Pedestrian not a Pedestrian?","created":"2024-05-14","cited_by_count":4,"area":null},{"id":"2107.04497","title":"Batch Inverse-Variance Weighting: Deep Heteroscedastic Regression","created":"2021-07-09","cited_by_count":3,"area":null},{"id":"2202.06505","title":"Opinions Vary? Diagnosis First!","created":"2022-02-14","cited_by_count":3,"area":null},{"id":"2202.09579","title":"Tripartite: Tackle Noisy Labels by a More Precise Partition","created":"2022-02-19","cited_by_count":3,"area":null},{"id":"2208.03016","title":"Calibrate the inter-observer segmentation uncertainty via diagnosis-first principle","created":"2022-08-05","cited_by_count":3,"area":null},{"id":"2210.00583","title":"The Dynamic of Consensus in Deep Networks and the Identification of Noisy Labels","created":"2022-10-02","cited_by_count":3,"area":null},{"id":"2405.03386","title":"Annot-Mix: Learning with Noisy Class Labels from Multiple Annotators via a Mixup Extension","created":"2024-05-06","cited_by_count":3,"area":null},{"id":"2407.06902","title":"Learning From Crowdsourced Noisy Labels: A Signal Processing Perspective","created":"2024-07-09","cited_by_count":3,"area":null},{"id":"2407.20950","title":"dopanim: A Dataset of Doppelganger Animals with Noisy Annotations from Multiple Humans","created":"2024-07-30","cited_by_count":3,"area":null},{"id":"2512.09315","title":"Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook","created":"2025-12-10","cited_by_count":3,"area":null},{"id":"2107.05039","title":"Learning from Crowds with Sparse and Imbalanced Annotations","created":"2021-07-11","cited_by_count":2,"area":null},{"id":"2109.03596","title":"Learn2Agree: Fitting with Multiple Annotators without Objective Ground Truth","created":"2021-09-08","cited_by_count":2,"area":null},{"id":"2110.12197","title":"Towards a Robust Differentiable Architecture Search under Label Noise","created":"2021-10-23","cited_by_count":2,"area":null},{"id":"2212.00601","title":"Multi-rater Prism: Learning self-calibrated medical image segmentation from multiple raters","created":"2022-12-01","cited_by_count":2,"area":null},{"id":"2212.08639","title":"An annotated instance segmentation XXL-CT data-set from a historic airplane","created":"2022-12-16","cited_by_count":2,"area":null},{"id":"2306.11911","title":"LNL+K: Enhancing Learning with Noisy Labels Through Noise Source Knowledge Integration","created":"2023-06-20","cited_by_count":2,"area":null},{"id":"2307.16889","title":"Rethinking Noisy Label Learning in Real-world Annotation Scenarios from the Noise-type Perspective","created":"2023-07-28","cited_by_count":2,"area":null},{"id":"2309.09742","title":"Drawing the Same Bounding Box Twice? Coping Noisy Annotations in Object Detection with Repeated Labels","created":"2023-09-18","cited_by_count":2,"area":null},{"id":"2310.11077","title":"United We Stand: Using Epoch-wise Agreement of Ensembles to Combat Overfit","created":"2023-10-17","cited_by_count":2,"area":null},{"id":"2311.16481","title":"Elucidating and Overcoming the Challenges of Label Noise in Supervised Contrastive Learning","created":"2023-11-25","cited_by_count":2,"area":null},{"id":"2312.07679","title":"Bayesian Online Learning for Consensus Prediction","created":"2023-12-12","cited_by_count":2,"area":null},{"id":"2408.14284","title":"May the Forgetting Be with You: Alternate Replay for Learning with Noisy Labels","created":"2024-08-26","cited_by_count":2,"area":null},{"id":"2410.21014","title":"Informed Deep Abstaining Classifier: Investigating noise-robust training for diagnostic decision support systems","created":"2024-10-28","cited_by_count":2,"area":null},{"id":"2504.02996","title":"Noise-Aware Generalization: Robustness to In-Domain Noise and Out-of-Domain Generalization","created":"2025-04-03","cited_by_count":2,"area":null},{"id":"2508.10393","title":"A Unified Evaluation Framework for Multi-Annotator Tendency Learning","created":"2025-08-14","cited_by_count":2,"area":null},{"id":"2106.15597","title":"Segmentation with Multiple Acceptable Annotations: A Case Study of Myocardial Segmentation in Contrast Echocardiography","created":"2021-06-29","cited_by_count":1,"area":null},{"id":"2108.02032","title":"Multi-Label Gold Asymmetric Loss Correction with Single-Label Regulators","created":"2021-08-04","cited_by_count":1,"area":null},{"id":"2110.11425","title":"A Machine Learning Framework Towards Transparency in Experts' Decision Quality","created":"2021-10-21","cited_by_count":1,"area":null},{"id":"2111.00734","title":"Robust Deep Learning from Crowds with Belief Propagation","created":"2021-11-01","cited_by_count":1,"area":null},{"id":"2206.05284","title":"Decoupling Predictions in Distributed Learning for Multi-Center Left Atrial MRI Segmentation","created":"2022-06-10","cited_by_count":1,"area":null},{"id":"2301.00545","title":"Knockoffs-SPR: Clean Sample Selection in Learning with Noisy Labels","created":"2023-01-02","cited_by_count":1,"area":null},{"id":"2303.16507","title":"Improving Object Detection in Medical Image Analysis through Multiple Expert Annotators: An Empirical Investigation","created":"2023-03-29","cited_by_count":1,"area":null},{"id":"2312.12937","title":"Robust Loss Functions for Training Decision Trees with Noisy Labels","created":"2023-12-20","cited_by_count":1,"area":null},{"id":"2406.12293","title":"Unleashing the Potential of Open-set Noisy Samples Against Label Noise for Medical Image Classification","created":"2024-06-18","cited_by_count":1,"area":null},{"id":"2410.16057","title":"Label Filling via Mixed Supervision for Medical Image Segmentation from Noisy Annotations","created":"2024-10-21","cited_by_count":1,"area":null},{"id":"2508.02495","title":"Rep-GLS: Report-Guided Generalized Label Smoothing for Robust Disease Detection","created":"2025-08-04","cited_by_count":1,"area":null},{"id":"2510.12687","title":"EReLiFM: Evidential Reliability-Aware Residual Flow Meta-Learning for Open-Set Domain Generalization under Noisy Labels","created":"2025-10-14","cited_by_count":1,"area":null},{"id":"2602.04574","title":"Probabilistic Label Spreading: Efficient and Consistent Estimation of Soft Labels with Epistemic Uncertainty on Graphs","created":"2026-02-04","cited_by_count":1,"area":null},{"id":"2608.14766","title":"Beyond Boundary Noise: Aggregated Aleatoric Uncertainty Fails to Capture Presence Ambiguity in 3D Lung Nodule Segmentation","created":"2026-08-14","cited_by_count":1,"area":null},{"id":"2103.00813","title":"DST: Data Selection and joint Training for Learning with Noisy Labels","created":"2021-03-01","cited_by_count":0,"area":null},{"id":"2104.05570","title":"Learning from Subjective Ratings Using Auto-Decoded Deep Latent Embeddings","created":"2021-04-12","cited_by_count":0,"area":null},{"id":"2107.07341","title":"Utilizing a digital swarm intelligence platform to improve consensus among radiologists and exploring its applications","created":"2021-06-26","cited_by_count":0,"area":null},{"id":"2112.15411","title":"Disjoint Contrastive Regression Learning for Multi-Sourced Annotations","created":"2021-12-31","cited_by_count":0,"area":null},{"id":"2301.00524","title":"Learning Confident Classifiers in the Presence of Label Noise","created":"2023-01-02","cited_by_count":0,"area":null},{"id":"2305.07430","title":"Expertise-based Weighting for Regression Models with Noisy Labels","created":"2023-05-12","cited_by_count":0,"area":null},{"id":"2404.04800","title":"Coordinated Sparse Recovery of Label Noise","created":"2024-04-07","cited_by_count":0,"area":null},{"id":"2407.17164","title":"Robust Deep Hawkes Process under Label Noise of Both Event and Occurrence","created":"2024-07-24","cited_by_count":0,"area":null}],"next_cursor":"100"}