{"total":19,"rows":[{"id":"2404.04518","title":"MedIAnomaly: A comparative study of anomaly detection in medical images","created":"2024-04-06","cited_by_count":26,"area":null},{"id":"2407.12474","title":"Leveraging the Mahalanobis Distance to enhance Unsupervised Brain MRI Anomaly Detection","created":"2024-07-17","cited_by_count":10,"area":null},{"id":"2412.17586","title":"Enhancing Reconstruction-Based Out-of-Distribution Detection in Brain MRI with Model and Metric Ensembles","created":"2024-12-23","cited_by_count":4,"area":null},{"id":"2404.07622","title":"Language Models Meet Anomaly Detection for Better Interpretability and Generalizability","created":"2024-04-11","cited_by_count":3,"area":null},{"id":"2411.04004","title":"Synomaly Noise and Multi-Stage Diffusion: A Novel Approach for Unsupervised Anomaly Detection in Medical Images","created":"2024-11-06","cited_by_count":3,"area":null},{"id":"2407.14191","title":"Normative Diffusion Autoencoders: Application to Amyotrophic Lateral Sclerosis","created":"2024-07-19","cited_by_count":1,"area":null},{"id":"2410.03306","title":"Selective Test-Time Adaptation for Unsupervised Anomaly Detection using Neural Implicit Representations","created":"2024-10-04","cited_by_count":1,"area":null},{"id":"2506.22504","title":"Patch2Loc: Learning to Localize Patches for Unsupervised Brain Lesion Detection","created":"2025-06-25","cited_by_count":1,"area":null},{"id":"2510.14462","title":"Unsupervised Deep Generative Models for Anomaly Detection in Neuroimaging: A Systematic Scoping Review","created":"2025-10-16","cited_by_count":1,"area":null},{"id":"2407.00148","title":"Localizing Anomalies via Multiscale Score Matching Analysis","created":"2024-06-28","cited_by_count":0,"area":null},{"id":"2408.08228","title":"Rethinking Medical Anomaly Detection in Brain MRI: An Image Quality Assessment Perspective","created":"2024-08-15","cited_by_count":0,"area":null},{"id":"2502.08321","title":"Screener: Self-supervised Pathology Segmentation in Medical CT Images","created":"2025-02-12","cited_by_count":0,"area":null},{"id":"2503.13400","title":"U2AD: Uncertainty-based Unsupervised Anomaly Detection Framework for Detecting T2 Hyperintensity in MRI Spinal Cord","created":"2025-03-17","cited_by_count":0,"area":null},{"id":"2510.08052","title":"RASALoRE: Region Aware Spatial Attention with Location-based Random Embeddings for Weakly Supervised Anomaly Detection in Brain MRI Scans","created":"2025-10-09","cited_by_count":0,"area":null},{"id":"2511.12899","title":"FDP: A Frequency-Decomposition Preprocessing Pipeline for Unsupervised Anomaly Detection in Brain MRI","created":"2025-11-17","cited_by_count":0,"area":null},{"id":"2602.06179","title":"Unsupervised Anomaly Detection of Diseases in the Female Pelvis for Real-Time MR Imaging","created":"2026-02-05","cited_by_count":0,"area":null},{"id":"2605.27306","title":"Normal Guidance is what Attention Needs","created":"2026-05-26","cited_by_count":0,"area":null},{"id":"2606.18749","title":"Toward Training-Free Zero-Shot Anomaly Detection in 3D Medical Images: A Batch-Based Approach Using 2D Foundation Models","created":"2026-06-17","cited_by_count":0,"area":null},{"id":"2610.02136","title":"MIRTO: a registration-gated, multiverse-tested evaluation protocol for unsupervised anomaly segmentation in brain MRI","created":"2026-10-01","cited_by_count":0,"area":null}],"next_cursor":null}