# Proxy-bridged Image Reconstruction Network for Anomaly Detection in Medical Images

arXiv: https://arxiv.org/abs/2110.01761 · HTML: https://mlsanity.com/paper/2110.01761/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 57 references that are in the map.
- First posted on arXiv 5 Oct 2021.

## Builds on (most cited first, 23 of 23)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection](https://mlsanity.com/paper/1904.02639/) (2019)
- [GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training](https://mlsanity.com/paper/1805.06725/) (2018)
- [Deep Anomaly Detection Using Geometric Transformations](https://mlsanity.com/paper/1805.10917/) (2018)
- [Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images](https://mlsanity.com/paper/1804.04488/) (2018)
- [Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoders](https://mlsanity.com/paper/1806.04972/) (2018)
- [Unsupervised Anomaly Localization using Variational Auto-Encoders](https://mlsanity.com/paper/1907.02796/) (2019)
- [Unsupervised Lesion Detection via Image Restoration with a Normative Prior](https://mlsanity.com/paper/2005.00031/) (2020)
- [Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection](https://mlsanity.com/paper/1812.05941/) (2018)
- [Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Images](https://mlsanity.com/paper/2008.03632/) (2020)
- [MADGAN: unsupervised Medical Anomaly Detection GAN using multiple adjacent brain MRI slice reconstruction](https://mlsanity.com/paper/2007.13559/) (2020)
- [Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT](https://mlsanity.com/paper/1905.12806/) (2019)
- [CANet: Cross-disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading](https://mlsanity.com/paper/1911.01376/) (2019)
- [Deep Mining External Imperfect Data for Chest X-ray Disease Screening](https://mlsanity.com/paper/2006.03796/) (2020)
- [Sparse-GAN: Sparsity-constrained Generative Adversarial Network for Anomaly Detection in Retinal OCT Image](https://mlsanity.com/paper/1911.12527/) (2019)
- [DeScarGAN: Disease-Specific Anomaly Detection with Weak Supervision](https://mlsanity.com/paper/2007.14118/) (2020)
- [Multi-Cell Multi-Task Convolutional Neural Networks for Diabetic Retinopathy Grading](https://mlsanity.com/paper/1808.10564/) (2018)
- [Abnormal Chest X-ray Identification With Generative Adversarial One-Class Classifier](https://mlsanity.com/paper/1903.02040/) (2019)

## Built on by (most cited first, 18 of 18)

- [Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images](https://mlsanity.com/paper/2403.12570/) (2024)
- [Learning Global-Local Correspondence with Semantic Bottleneck for Logical Anomaly Detection](https://mlsanity.com/paper/2303.05768/) (2023)
- [Progressive Boundary Guided Anomaly Synthesis for Industrial Anomaly Detection](https://mlsanity.com/paper/2412.17458/) (2024)
- [Siamese Transition Masked Autoencoders as Uniform Unsupervised Visual Anomaly Detector](https://mlsanity.com/paper/2211.00349/) (2022)
- [Robust Vector Quantized-Variational Autoencoder](https://mlsanity.com/paper/2202.01987/) (2022)
- [Position-Guided Prompt Learning for Anomaly Detection in Chest X-Rays](https://mlsanity.com/paper/2405.11976/) (2024)
- [SPACE: SPAtial-aware Consistency rEgularization for anomaly detection in Industrial applications](https://mlsanity.com/paper/2411.05822/) (2024)
- [Region and Spatial Aware Anomaly Detection for Fundus Images](https://mlsanity.com/paper/2303.03817/) (2023)
- [Image Reconstruction using Superpixel Clustering and Tensor Completion](https://mlsanity.com/paper/2305.09564/) (2023)
- [Diff-CXR: Report-to-CXR generation through a disease-knowledge enhanced diffusion model](https://mlsanity.com/paper/2410.20165/) (2024)
- [URA-Net: Uncertainty-Integrated Anomaly Perception and Restoration Attention Network for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2603.22840/) (2026)
- [Harnessing Intra-group Variations Via a Population-Level Context for Pathology Detection](https://mlsanity.com/paper/2403.02307/) (2024)
- [An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited Data](https://mlsanity.com/paper/2407.11500/) (2024)
- [SD-MAD: Sign-Driven Few-shot Multi-Anomaly Detection in Medical Images](https://mlsanity.com/paper/2505.16659/) (2025)
- [A Closer Look at Edema Area Segmentation in SD-OCT Images Using Adversarial Framework](https://mlsanity.com/paper/2508.18790/) (2025)
- [Cross-Task Generalization in Handwriting-Based Alzheimer's Screening via Vision Language Adaptation](https://mlsanity.com/paper/2511.05841/) (2025)
- [Unsupervised Anomaly Detection in Brain MRI via Disentangled Anatomy Learning](https://mlsanity.com/paper/2512.21924/) (2025)
- [MTL-MAD: Multi-Task Learners are Effective Medical Anomaly Detectors](https://mlsanity.com/paper/2605.05891/) (2026)

## Abstract (arXiv)

Anomaly detection in medical images refers to the identification of abnormal images with only normal images in the training set. Most existing methods solve this problem with a self-reconstruction framework, which tends to learn an identity mapping and reduces the sensitivity to anomalies. To mitigate this problem, in this paper, we propose a novel Proxy-bridged Image Reconstruction Network (ProxyAno) for anomaly detection in medical images. Specifically, we use an intermediate proxy to bridge the input image and the reconstructed image. We study different proxy types, and we find that the superpixel-image (SI) is the best one. We set all pixels' intensities within each superpixel as their average intensity, and denote this image as SI. The proposed ProxyAno consists of two modules, a Proxy Extraction Module and an Image Reconstruction Module. In the Proxy Extraction Module, a memory is introduced to memorize the feature correspondence for normal image to its corresponding SI, while the memorized correspondence does not apply to the abnormal images, which leads to the information loss for abnormal image and facilitates the anomaly detection. In the Image Reconstruction Module, we map an SI to its reconstructed image. Further, we crop a patch from the image and paste it on the normal SI to mimic the anomalies, and enforce the network to reconstruct the normal image even with the pseudo abnormal SI. In this way, our network enlarges the reconstruction error for anomalies. Extensive experiments on brain MR images, retinal OCT images and retinal fundus images verify the effectiveness of our method for both image-level and pixel-level anomaly detection.