mlsanityData to 7 Oct 2026

SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection

arXiv 2111.13495 ↗First posted 26 Nov 2021cs.CVcited by 34

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Abstract (arXiv)

Radiography imaging protocols focus on particular body regions, therefore producing images of great similarity and yielding recurrent anatomical structures across patients. To exploit this structured information, we propose the use of Space-aware Memory Queues for In-painting and Detecting anomalies from radiography images (abbreviated as SQUID). We show that SQUID can taxonomize the ingrained anatomical structures into recurrent patterns; and in the inference, it can identify anomalies (unseen/modified patterns) in the image. SQUID surpasses 13 state-of-the-art methods in unsupervised anomaly detection by at least 5 points on two chest X-ray benchmark datasets measured by the Area Under the Curve (AUC). Additionally, we have created a new dataset (DigitAnatomy), which synthesizes the spatial correlation and consistent shape in chest anatomy. We hope DigitAnatomy can prompt the development, evaluation, and interpretability of anomaly detection methods.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). Builds on 59 of its 93 references that are in the map. First posted on arXiv 26 Nov 2021.

Builds on

59 of 93 references.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 4; 2024: 14; 2025: 9; 2026: 5.

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  1. 2024Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Promptscited by 88
  2. 2024Towards Generalizable Tumor Synthesiscited by 65
  3. 2024AbdomenAtlas: A Large-Scale, Detailed-Annotated, & Multi-Center Dataset for Efficient Transfer Learning and Open Algorithmic Benchmarkingcited by 64
  4. 2024MediCLIP: Adapting CLIP for Few-shot Medical Image Anomaly Detectioncited by 35
  5. 2023Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detectioncited by 25
  6. 2024Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detectioncited by 22
  7. 2023Learning Global-Local Correspondence with Semantic Bottleneck for Logical Anomaly Detectioncited by 21
  8. 2025Learning to Detect Multi-class Anomalies with Just One Normal Image Promptcited by 18
  9. 2024Continuous Memory Representation for Anomaly Detectioncited by 16
  10. 2024SAM-LAD: Segment Anything Model Meets Zero-Shot Logic Anomaly Detectioncited by 14
  11. 2024FreeTumor: Advance Tumor Segmentation via Large-Scale Tumor Synthesiscited by 14
  12. 2024Exploiting Structural Consistency of Chest Anatomy for Unsupervised Anomaly Detection in Radiography Imagescited by 13
22 more
  1. 2022Anatomy-aware Self-supervised Learning for Anomaly Detection in Chest Radiographscited by 12
  2. 2024FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Datacited by 8
  3. 2025Foundation Models and Transformers for Anomaly Detection: A Surveycited by 8
  4. 2025Towards Accurate Unified Anomaly Segmentationcited by 7
  5. 2025Scale-Aware Contrastive Reverse Distillation for Unsupervised Medical Anomaly Detectioncited by 6
  6. 2022ADPS: Asymmetric Distillation Post-Segmentation for Image Anomaly Detectioncited by 5
  7. 2023Neural Network Training Strategy to Enhance Anomaly Detection Performance: A Perspective on Reconstruction Loss Amplificationcited by 4
  8. 2024Position-Guided Prompt Learning for Anomaly Detection in Chest X-Rayscited by 3
  9. 2025Focus on Texture: Rethinking Pre-training in Masked Autoencoders for Medical Image Classificationcited by 3
  10. 2024A Recover-then-Discriminate Framework for Robust Anomaly Detectioncited by 2
  11. 2023Weakly Supervised Anomaly Detection for Chest X-Ray Imagecited by 1
  12. 2024Spatial-aware Attention Generative Adversarial Network for Semi-supervised Anomaly Detection in Medical Imagecited by 1
  13. 2025IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domaincited by 1
  14. 2025Multi-class Image Anomaly Detection for Practical Applications: Requirements and Robust Solutionscited by 1
  15. 2025ART-ASyn: Anatomy-aware Realistic Texture-based Anomaly Synthesis Framework for Chest X-Rayscited by 1
  16. 2024A Mallows-like Criterion for Anomaly Detection with Random Forest Implementationcited by 0
  17. 2025SD-MAD: Sign-Driven Few-shot Multi-Anomaly Detection in Medical Imagescited by 0
  18. 2026BAAF: Universal Transformation of One-Class Classifiers for Unsupervised Image Anomaly Detectioncited by 0
  19. 2026PDD: Manifold-Prior Diverse Distillation for Medical Anomaly Detectioncited by 0
  20. 2026MedSAD-CLIP: Supervised CLIP with Token-Patch Cross-Attention for Medical Anomaly Detection and Segmentationcited by 0
  21. 2026AUCp: Pseudo-AUC for Inference Model Selection with Unlabeled Validation Data in Abnormality Detectioncited by 0
  22. 2026LogiCo: A Unified Framework for Logical and Structural Anomaly Detectioncited by 0