{"total":57,"rows":[{"id":"2403.19949","title":"FairCLIP: Harnessing Fairness in Vision-Language Learning","created":"2024-03-29","cited_by_count":69,"area":null},{"id":"2201.08657","title":"Enhancing Pseudo Label Quality for Semi-Supervised Domain-Generalized Medical Image Segmentation","created":"2022-01-21","cited_by_count":51,"area":null},{"id":"2407.00983","title":"FairMedFM: Fairness Benchmarking for Medical Imaging Foundation Models","created":"2024-07-01","cited_by_count":47,"area":null},{"id":"2108.12043","title":"Learning Disentangled Representations in the Imaging Domain","created":"2021-08-26","cited_by_count":46,"area":null},{"id":"2106.13292","title":"Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image Segmentation","created":"2021-06-24","cited_by_count":36,"area":null},{"id":"2209.13177","title":"Addressing Fairness Issues in Deep Learning-Based Medical Image Analysis: A Systematic Review","created":"2022-09-27","cited_by_count":28,"area":null},{"id":"2203.10977","title":"Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis","created":"2022-03-21","cited_by_count":25,"area":null},{"id":"2208.10013","title":"FairDisCo: Fairer AI in Dermatology via Disentanglement Contrastive Learning","created":"2022-08-22","cited_by_count":22,"area":null},{"id":"2110.00603","title":"Algorithm Fairness in AI for Medicine and Healthcare","created":"2021-10-01","cited_by_count":21,"area":null},{"id":"2303.08325","title":"FairAdaBN: Mitigating unfairness with adaptive batch normalization and its application to dermatological disease classification","created":"2023-03-15","cited_by_count":19,"area":null},{"id":"2208.13528","title":"CIRCLe: Color Invariant Representation Learning for Unbiased Classification of Skin Lesions","created":"2022-08-29","cited_by_count":17,"area":null},{"id":"2311.13628","title":"Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models","created":"2023-11-22","cited_by_count":17,"area":null},{"id":"2208.06613","title":"A Study of Demographic Bias in CNN-based Brain MR Segmentation","created":"2022-08-13","cited_by_count":16,"area":null},{"id":"2303.03242","title":"Evaluating the Fairness of Deep Learning Uncertainty Estimates in Medical Image Analysis","created":"2023-03-06","cited_by_count":15,"area":null},{"id":"2109.09658","title":"FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging","created":"2021-09-20","cited_by_count":13,"area":null},{"id":"2209.01627","title":"A systematic study of race and sex bias in CNN-based cardiac MR segmentation","created":"2022-09-04","cited_by_count":9,"area":null},{"id":"2308.13415","title":"An investigation into the impact of deep learning model choice on sex and race bias in cardiac MR segmentation","created":"2023-08-25","cited_by_count":8,"area":null},{"id":"2211.15997","title":"MedalCare-XL: 16,900 healthy and pathological 12 lead ECGs obtained through electrophysiological simulations","created":"2022-11-29","cited_by_count":6,"area":null},{"id":"2408.06890","title":"BMFT: Achieving Fairness via Bias-based Weight Masking Fine-tuning","created":"2024-08-13","cited_by_count":6,"area":null},{"id":"2309.14392","title":"Unveiling Fairness Biases in Deep Learning-Based Brain MRI Reconstruction","created":"2023-09-25","cited_by_count":5,"area":null},{"id":"2506.17787","title":"Incorporating Rather Than Eliminating: Achieving Fairness for Skin Disease Diagnosis Through Group-Specific Expert","created":"2025-06-21","cited_by_count":5,"area":null},{"id":"2307.08411","title":"Neurosymbolic AI for Reasoning on Biomedical Knowledge Graphs","created":"2023-07-17","cited_by_count":4,"area":null},{"id":"2311.02115","title":"Towards objective and systematic evaluation of bias in artificial intelligence for medical imaging","created":"2023-11-03","cited_by_count":4,"area":null},{"id":"2408.02676","title":"On Biases in a UK Biobank-based Retinal Image Classification Model","created":"2024-07-30","cited_by_count":4,"area":null},{"id":"2412.16373","title":"FairREAD: Re-fusing Demographic Attributes after Disentanglement for Fair Medical Image Classification","created":"2024-12-20","cited_by_count":4,"area":null},{"id":"2403.06104","title":"Debiased Noise Editing on Foundation Models for Fair Medical Image Classification","created":"2024-03-10","cited_by_count":3,"area":null},{"id":"2403.11464","title":"FedSPU: Personalized Federated Learning for Resource-constrained Devices with Stochastic Parameter Update","created":"2024-03-18","cited_by_count":3,"area":null},{"id":"2510.17999","title":"Investigating Demographic Bias in Brain MRI Segmentation: A Comparative Study of Deep-Learning and Non-Deep-Learning Methods","created":"2025-10-20","cited_by_count":3,"area":null},{"id":"2403.05114","title":"APPLE: Adversarial Privacy-aware Perturbations on Latent Embedding for Unfairness Mitigation","created":"2024-03-08","cited_by_count":2,"area":null},{"id":"2407.16953","title":"Open Challenges on Fairness of Artificial Intelligence in Medical Imaging Applications","created":"2024-07-24","cited_by_count":2,"area":null},{"id":"2510.01296","title":"From 2D to 3D, Deep Learning-based Shape Reconstruction in Magnetic Resonance Imaging: A Review","created":"2025-10-01","cited_by_count":2,"area":null},{"id":"2211.08672","title":"Mitigating Urban-Rural Disparities in Contrastive Representation Learning with Satellite Imagery","created":"2022-11-16","cited_by_count":1,"area":null},{"id":"2309.00451","title":"Unsupervised bias discovery in medical image segmentation","created":"2023-09-01","cited_by_count":1,"area":null},{"id":"2408.02462","title":"An investigation into the causes of race bias in AI-based cine CMR segmentation","created":"2024-08-05","cited_by_count":1,"area":null},{"id":"2409.15888","title":"Investigating Gender Bias in Lymph-node Segmentation with Anatomical Priors","created":"2024-09-24","cited_by_count":1,"area":null},{"id":"2503.17089","title":"Understanding-informed Bias Mitigation for Fair CMR Segmentation","created":"2025-03-21","cited_by_count":1,"area":null},{"id":"2510.27421","title":"Who Does Your Algorithm Fail? Investigating Age and Ethnic Bias in the MAMA-MIA Dataset","created":"2025-10-31","cited_by_count":1,"area":null},{"id":"2603.04113","title":"Understanding Sources of Demographic Predictability in Brain MRI via Disentangling Anatomy and Contrast","created":"2026-03-04","cited_by_count":1,"area":null},{"id":"2206.14919","title":"Identifying and Combating Bias in Segmentation Networks by leveraging multiple resolutions","created":"2022-06-29","cited_by_count":0,"area":null},{"id":"2308.00231","title":"Capsa: A Unified Framework for Quantifying Risk in Deep Neural Networks","created":"2023-08-01","cited_by_count":0,"area":null},{"id":"2309.06807","title":"Bayesian uncertainty-weighted loss for improved generalisability on polyp segmentation task","created":"2023-09-13","cited_by_count":0,"area":null},{"id":"2309.17197","title":"An Investigation Into Race Bias in Random Forest Models Based on Breast DCE-MRI Derived Radiomics Features","created":"2023-09-29","cited_by_count":0,"area":null},{"id":"2310.02829","title":"All Sizes Matter: Improving Volumetric Brain Segmentation on Small Lesions","created":"2023-10-04","cited_by_count":0,"area":null},{"id":"2311.07234","title":"Multi-task learning for joint weakly-supervised segmentation and aortic arch anomaly classification in fetal cardiac MRI","created":"2023-11-13","cited_by_count":0,"area":null},{"id":"2411.02867","title":"AtlasSeg: Atlas Prior Guided Dual-U-Net for Cortical Segmentation in Fetal Brain MRI","created":"2024-11-05","cited_by_count":0,"area":null},{"id":"2412.16542","title":"FairDD: Enhancing Fairness with domain-incremental learning in dermatological disease diagnosis","created":"2024-12-21","cited_by_count":0,"area":null},{"id":"2504.14882","title":"Some Optimizers are More Equal: Understanding the Role of Optimizers in Group Fairness","created":"2025-04-21","cited_by_count":0,"area":null},{"id":"2508.18826","title":"SWiFT: Soft-Mask Weight Fine-tuning for Bias Mitigation","created":"2025-08-26","cited_by_count":0,"area":null},{"id":"2508.18886","title":"Toward Robust Medical Fairness: Debiased Dual-Modal Alignment via Text-Guided Attribute-Disentangled Prompt Learning for Vision-Language Models","created":"2025-08-26","cited_by_count":0,"area":"mllm"},{"id":"2509.11184","title":"The Impact of Skin Tone Label Granularity on the Performance and Fairness of AI Based Dermatology Image Classification Models","created":"2025-09-14","cited_by_count":0,"area":null},{"id":"2510.19893","title":"EQPO: Equitable Group Relative Policy Optimization for Clinical Reasoning","created":"2025-10-22","cited_by_count":0,"area":null},{"id":"2601.13094","title":"Patient-Conditioned Adaptive Offsets for Reliable Diagnosis across Subgroups","created":"2026-01-19","cited_by_count":0,"area":null},{"id":"2604.02031","title":"Rare-Aware Autoencoding: Reconstructing Spatially Imbalanced Data","created":"2026-04-02","cited_by_count":0,"area":null},{"id":"2604.13326","title":"Right Regions, Wrong Labels: Semantic Label Flips in Segmentation under Correlation Shift","created":"2026-04-14","cited_by_count":0,"area":null},{"id":"2605.01706","title":"Exploring Entropy-based Active Learning for Fair Brain Segmentation","created":"2026-05-03","cited_by_count":0,"area":null},{"id":"2607.02596","title":"CIPHER: Causal Intervention Pathways for Healthcare Equity and Robustness","created":"2026-07-01","cited_by_count":0,"area":null},{"id":"2607.07852","title":"False Confidence: Automated Labels Confound Fairness Audits in Cervical Spine Segmentation","created":"2026-07-08","cited_by_count":0,"area":null}],"next_cursor":null}