mlsanityData to 7 Oct 2026

Detecting Outliers with Foreign Patch Interpolation

arXiv 2011.04197 ↗First posted 9 Nov 2020cs.CVcited by 44

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Abstract (arXiv)

In medical imaging, outliers can contain hypo/hyper-intensities, minor deformations, or completely altered anatomy. To detect these irregularities it is helpful to learn the features present in both normal and abnormal images. However this is difficult because of the wide range of possible abnormalities and also the number of ways that normal anatomy can vary naturally. As such, we leverage the natural variations in normal anatomy to create a range of synthetic abnormalities. Specifically, the same patch region is extracted from two independent samples and replaced with an interpolation between both patches. The interpolation factor, patch size, and patch location are randomly sampled from uniform distributions. A wide residual encoder decoder is trained to give a pixel-wise prediction of the patch and its interpolation factor. This encourages the network to learn what features to expect normally and to identify where foreign patterns have been introduced. The estimate of the interpolation factor lends itself nicely to the derivation of an outlier score. Meanwhile the pixel-wise output allows for pixel- and subject- level predictions using the same model.

Key facts. Cited by 44 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Nov 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 8; 2023: 12; 2024: 6; 2025: 7; 2026: 5.

44 papers.

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  1. 2021Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localizationcited by 116
  2. 2023BMAD: Benchmarks for Medical Anomaly Detectioncited by 81
  3. 2021Detecting Outliers with Poisson Image Interpolationcited by 44
  4. 2021Confidence-based Out-of-Distribution Detection: A Comparative Study and Analysiscited by 34
  5. 2021CarveMix: A Simple Data Augmentation Method for Brain Lesion Segmentationcited by 30
  6. 2024MedIAnomaly: A comparative study of anomaly detection in medical imagescited by 25
  7. 2023The role of noise in denoising models for anomaly detection in medical imagescited by 23
  8. 2022Unsupervised Anomaly Localization with Structural Feature-Autoencoderscited by 22
  9. 2021Implicit field learning for unsupervised anomaly detection in medical imagescited by 17
  10. 2022Dual-Distribution Discrepancy for Anomaly Detection in Chest X-Rayscited by 17
  11. 2023On the use of Mahalanobis distance for out-of-distribution detection with neural networks for medical imagingcited by 15
  12. 2023Many tasks make light work: Learning to localise medical anomalies from multiple synthetic taskscited by 12
32 more
  1. 2023Modality Cycles with Masked Conditional Diffusion for Unsupervised Anomaly Segmentation in MRIcited by 12
  2. 2024Ensembled Cold-Diffusion Restorations for Unsupervised Anomaly Detectioncited by 12
  3. 2022On the Pitfalls of Using the Residual Error as Anomaly Scorecited by 11
  4. 2023REB: Reducing Biases in Representation for Industrial Anomaly Detectioncited by 9
  5. 2023AMAE: Adaptation of Pre-Trained Masked Autoencoder for Dual-Distribution Anomaly Detection in Chest X-Rayscited by 8
  6. 2023Unmasking the Chameleons: A Benchmark for Out-of-Distribution Detection in Medical Tabular Datacited by 8
  7. 2023DISYRE: Diffusion-Inspired SYnthetic REstoration for Unsupervised Anomaly Detectioncited by 8
  8. 2025PatchGuard: Adversarially Robust Anomaly Detection and Localization through Vision Transformers and Pseudo Anomaliescited by 8
  9. 2024Towards Universal Unsupervised Anomaly Detection in Medical Imagingcited by 6
  10. 2025REFLECT: Rectified Flows for Efficient Brain Anomaly Correction Transportcited by 5
  11. 2023Confidence-Aware and Self-Supervised Image Anomaly Localisationcited by 4
  12. 2024Position-Guided Prompt Learning for Anomaly Detection in Chest X-Rayscited by 3
  13. 2024Synomaly Noise and Multi-Stage Diffusion: A Novel Approach for Unsupervised Anomaly Detection in Medical Imagescited by 3
  14. 2022nnOOD: A Framework for Benchmarking Self-supervised Anomaly Localisation Methodscited by 2
  15. 2022Adnexal Mass Segmentation with Ultrasound Data Synthesiscited by 2
  16. 2023Domain knowledge-informed Synthetic fault sample generation with Health Data Map for cross-domain Planetary Gearbox Fault Diagnosiscited by 2
  17. 2025Prototypical Learning Guided Context-Aware Segmentation Network for Few-Shot Anomaly Detectioncited by 2
  18. 2022AutoSeg -- Steering the Inductive Biases for Automatic Pathology Segmentationcited by 1
  19. 2024Spatial-aware Attention Generative Adversarial Network for Semi-supervised Anomaly Detection in Medical Imagecited by 1
  20. 2025OCSVM-Guided Representation Learning for Unsupervised Anomaly Detectioncited by 1
  21. 2025ASBench: Image Anomalies Synthesis Benchmark for Anomaly Detectioncited by 1
  22. 2025ART-ASyn: Anatomy-aware Realistic Texture-based Anomaly Synthesis Framework for Chest X-Rayscited by 1
  23. 2021Pediatric Otoscopy Video Screening with Shift Contrastive Anomaly Detectioncited by 0
  24. 2022Dual-distribution discrepancy with self-supervised refinement for anomaly detection in medical imagescited by 0
  25. 2022Solving Sample-Level Out-of-Distribution Detection on 3D Medical Imagescited by 0
  26. 2023MIM-OOD: Generative Masked Image Modelling for Out-of-Distribution Detection in Medical Imagescited by 0
  27. 2025Unsupervised Anomaly Detection in Brain MRI via Disentangled Anatomy Learningcited by 0
  28. 2026The Invisible Gorilla Effect in Out-of-distribution Detectioncited by 0
  29. 2026Wasserstein-Aligned Localisation for VLM-Based Distributional OOD Detection in Medical Imagingcited by 0
  30. 2026MTL-MAD: Multi-Task Learners are Effective Medical Anomaly Detectorscited by 0
  31. 2026AUCp: Pseudo-AUC for Inference Model Selection with Unlabeled Validation Data in Abnormality Detectioncited by 0
  32. 2026FLASH: A "Generate Once, Synthesize Many" Framework for Synthetic Anomaly Generation in Industrial Anomaly Detectioncited by 0