# Detecting Outliers with Foreign Patch Interpolation

arXiv: https://arxiv.org/abs/2011.04197 · HTML: https://mlsanity.com/paper/2011.04197/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2020.

## Built on by (most cited first, 30 of 44)

- [Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization](https://mlsanity.com/paper/2109.15222/) (2021)
- [BMAD: Benchmarks for Medical Anomaly Detection](https://mlsanity.com/paper/2306.11876/) (2023)
- [Detecting Outliers with Poisson Image Interpolation](https://mlsanity.com/paper/2107.02622/) (2021)
- [Confidence-based Out-of-Distribution Detection: A Comparative Study and Analysis](https://mlsanity.com/paper/2107.02568/) (2021)
- [CarveMix: A Simple Data Augmentation Method for Brain Lesion Segmentation](https://mlsanity.com/paper/2108.06883/) (2021)
- [MedIAnomaly: A comparative study of anomaly detection in medical images](https://mlsanity.com/paper/2404.04518/) (2024)
- [Unsupervised Anomaly Localization with Structural Feature-Autoencoders](https://mlsanity.com/paper/2208.10992/) (2022)
- [The role of noise in denoising models for anomaly detection in medical images](https://mlsanity.com/paper/2301.08330/) (2023)
- [Implicit field learning for unsupervised anomaly detection in medical images](https://mlsanity.com/paper/2106.05214/) (2021)
- [Dual-Distribution Discrepancy for Anomaly Detection in Chest X-Rays](https://mlsanity.com/paper/2206.03935/) (2022)
- [On the use of Mahalanobis distance for out-of-distribution detection with neural networks for medical imaging](https://mlsanity.com/paper/2309.01488/) (2023)
- [Many tasks make light work: Learning to localise medical anomalies from multiple synthetic tasks](https://mlsanity.com/paper/2307.00899/) (2023)
- [Modality Cycles with Masked Conditional Diffusion for Unsupervised Anomaly Segmentation in MRI](https://mlsanity.com/paper/2308.16150/) (2023)
- [Ensembled Cold-Diffusion Restorations for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2407.06635/) (2024)
- [On the Pitfalls of Using the Residual Error as Anomaly Score](https://mlsanity.com/paper/2202.03826/) (2022)
- [REB: Reducing Biases in Representation for Industrial Anomaly Detection](https://mlsanity.com/paper/2308.12577/) (2023)
- [AMAE: Adaptation of Pre-Trained Masked Autoencoder for Dual-Distribution Anomaly Detection in Chest X-Rays](https://mlsanity.com/paper/2307.12721/) (2023)
- [Unmasking the Chameleons: A Benchmark for Out-of-Distribution Detection in Medical Tabular Data](https://mlsanity.com/paper/2309.16220/) (2023)
- [DISYRE: Diffusion-Inspired SYnthetic REstoration for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2311.15453/) (2023)
- [PatchGuard: Adversarially Robust Anomaly Detection and Localization through Vision Transformers and Pseudo Anomalies](https://mlsanity.com/paper/2506.09237/) (2025)
- [Towards Universal Unsupervised Anomaly Detection in Medical Imaging](https://mlsanity.com/paper/2401.10637/) (2024)
- [REFLECT: Rectified Flows for Efficient Brain Anomaly Correction Transport](https://mlsanity.com/paper/2508.02889/) (2025)
- [Confidence-Aware and Self-Supervised Image Anomaly Localisation](https://mlsanity.com/paper/2303.13227/) (2023)
- [Position-Guided Prompt Learning for Anomaly Detection in Chest X-Rays](https://mlsanity.com/paper/2405.11976/) (2024)
- [Synomaly Noise and Multi-Stage Diffusion: A Novel Approach for Unsupervised Anomaly Detection in Medical Images](https://mlsanity.com/paper/2411.04004/) (2024)
- [nnOOD: A Framework for Benchmarking Self-supervised Anomaly Localisation Methods](https://mlsanity.com/paper/2209.01124/) (2022)
- [Adnexal Mass Segmentation with Ultrasound Data Synthesis](https://mlsanity.com/paper/2209.12305/) (2022)
- [Domain knowledge-informed Synthetic fault sample generation with Health Data Map for cross-domain Planetary Gearbox Fault Diagnosis](https://mlsanity.com/paper/2305.19569/) (2023)
- [Prototypical Learning Guided Context-Aware Segmentation Network for Few-Shot Anomaly Detection](https://mlsanity.com/paper/2512.15319/) (2025)
- [AutoSeg -- Steering the Inductive Biases for Automatic Pathology Segmentation](https://mlsanity.com/paper/2201.09579/) (2022)

## 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.