mlsanityData to 7 Oct 2026

MedIAnomaly: A comparative study of anomaly detection in medical images

arXiv 2404.04518 ↗First posted 6 Apr 2024cs.CVcited by 25

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Abstract (arXiv)

Anomaly detection (AD) aims at detecting abnormal samples that deviate from the expected normal patterns. Generally, it can be trained merely on normal data, without a requirement for abnormal samples, and thereby plays an important role in rare disease recognition and health screening in the medical domain. Despite the emergence of numerous methods for medical AD, the lack of a fair and comprehensive evaluation causes ambiguous conclusions and hinders the development of this field. To address this problem, this paper builds a benchmark with unified comparison. Seven medical datasets with five image modalities, including chest X-rays, brain MRIs, retinal fundus images, dermatoscopic images, and histopathology images, are curated for extensive evaluation. Thirty typical AD methods, including reconstruction and self-supervised learning-based methods, are involved in comparison of image-level anomaly classification and pixel-level anomaly segmentation. Furthermore, for the first time, we systematically investigate the effect of key components in existing methods, revealing unresolved challenges and potential future directions. The datasets and code are available at https://github.com/caiyu6666/MedIAnomaly.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). Builds on 86 of its 119 references that are in the map. First posted on arXiv 6 Apr 2024.

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86 of 119 references.

References · 86

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 10; 2026: 12.

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  1. 2024Incomplete Multimodal Industrial Anomaly Detection via Cross-Modal Distillationcited by 6
  2. 2025Scale-Aware Contrastive Reverse Distillation for Unsupervised Medical Anomaly Detectioncited by 6
  3. 2024Revisiting Deep Ensemble Uncertainty for Enhanced Medical Anomaly Detectioncited by 4
  4. 2025A Survey on Video Anomaly Detection via Deep Learning: Human, Vehicle, and Environmentcited by 4
  5. 2026Dual Distillation for Few-Shot Anomaly Detectioncited by 3
  6. 2024Evaluating Self-Supervised Learning in Medical Imaging: A Benchmark for Robustness, Generalizability, and Multi-Domain Impactcited by 2
  7. 2025Q-Former Autoencoder: A Modern Framework for Medical Anomaly Detectioncited by 2
  8. 2025Quantitative Benchmarking of Anomaly Detection Methods in Digital Pathologycited by 1
  9. 2025BenchReAD: A systematic benchmark for retinal anomaly detectioncited by 1
  10. 2025Unsupervised anomaly detection using Bayesian flow networks: application to brain FDG PET in the context of Alzheimer's diseasecited by 1
  11. 2025OCSVM-Guided Representation Learning for Unsupervised Anomaly Detectioncited by 1
  12. 2025From Pixels to Graphs: Deep Graph-Level Anomaly Detection on Dermoscopic Imagescited by 1
13 more
  1. 2025FedDyMem: Efficient Federated Learning with Dynamic Memory and Memory-Reduce for Unsupervised Image Anomaly Detectioncited by 0
  2. 2025Unsupervised Anomaly Detection in Brain MRI via Disentangled Anatomy Learningcited by 0
  3. 2026Unsupervised Anomaly Detection of Diseases in the Female Pelvis for Real-Time MR Imagingcited by 0
  4. 2026PDD: Manifold-Prior Diverse Distillation for Medical Anomaly Detectioncited by 0
  5. 2026Latent Anomaly Knowledge Excavation: Unveiling Sparse Sensitive Neurons in Vision-Language Modelscited by 0
  6. 2026Anatomy-Aware Unsupervised Detection and Localization of Retinal Abnormalities in Optical Coherence Tomographycited by 0
  7. 2026MTL-MAD: Multi-Task Learners are Effective Medical Anomaly Detectorscited by 0
  8. 2026AUCp: Pseudo-AUC for Inference Model Selection with Unlabeled Validation Data in Abnormality Detectioncited by 0
  9. 2026HiMatch-AD: DINOv3-driven Hierarchical Matching for Training-free Medical Anomaly Detectioncited by 0
  10. 2026Super-Generalist: Towards Comprehensive and Accurate Medical Image Understanding via Generalist-Specialist Synergycited by 0
  11. 2026Memory-Bounded Continuation of Greedy Sampling for Continual Anomaly Detectioncited by 0
  12. 2026GeoMAD: Geometry-Aware Multi-View Anomaly Detection via Deformable Fusion and Distributional Alignmentcited by 0
  13. 2026Interpretable Multi-Hypersphere Deep Anomaly Detection for Open-set Supervised Anomaly Detectioncited by 0