# AFSC: Adaptive Fourier Space Compression for Anomaly Detection

arXiv: https://arxiv.org/abs/2204.07963 · HTML: https://mlsanity.com/paper/2204.07963/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 42 references that are in the map.
- First posted on arXiv 17 Apr 2022.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [TSM: Temporal Shift Module for Efficient Video Understanding](https://mlsanity.com/paper/1811.08383/) (2018)
- [Towards Total Recall in Industrial Anomaly Detection](https://mlsanity.com/paper/2106.08265/) (2021)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection](https://mlsanity.com/paper/2108.07610/) (2021)
- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings](https://mlsanity.com/paper/1911.02357/) (2019)
- [Multiresolution Knowledge Distillation for Anomaly Detection](https://mlsanity.com/paper/2011.11108/) (2020)
- [The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization](https://mlsanity.com/paper/2112.09045/) (2021)
- [Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study](https://mlsanity.com/paper/2004.03271/) (2020)
- [Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection](https://mlsanity.com/paper/2203.05550/) (2022)
- [ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly Segmentation](https://mlsanity.com/paper/2103.03664/) (2021)

## Built on by (most cited first, 12 of 12)

- [U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation](https://mlsanity.com/paper/2406.02918/) (2024)
- [Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images](https://mlsanity.com/paper/2403.12570/) (2024)
- [Endora: Video Generation Models as Endoscopy Simulators](https://mlsanity.com/paper/2403.11050/) (2024)
- [LGS: A Light-weight 4D Gaussian Splatting for Efficient Surgical Scene Reconstruction](https://mlsanity.com/paper/2406.16073/) (2024)
- [EndoSparse: Real-Time Sparse View Synthesis of Endoscopic Scenes using Gaussian Splatting](https://mlsanity.com/paper/2407.01029/) (2024)
- [GaussianStego: A Generalizable Stenography Pipeline for Generative 3D Gaussians Splatting](https://mlsanity.com/paper/2407.01301/) (2024)
- [GTP-4o: Modality-prompted Heterogeneous Graph Learning for Omni-modal Biomedical Representation](https://mlsanity.com/paper/2407.05540/) (2024)
- [When 3D Partial Points Meets SAM: Tooth Point Cloud Segmentation with Sparse Labels](https://mlsanity.com/paper/2409.01691/) (2024)
- [Exploiting Point-Language Models with Dual-Prompts for 3D Anomaly Detection](https://mlsanity.com/paper/2502.11307/) (2025)
- [A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospects](https://mlsanity.com/paper/2507.13378/) (2025)
- [Beyond Sparse Supervision: Diffusion-Guided Learning for Few-Shot Graph Fraud Detection](https://mlsanity.com/paper/2606.28134/) (2026)
- [SD-MAD: Sign-Driven Few-shot Multi-Anomaly Detection in Medical Images](https://mlsanity.com/paper/2505.16659/) (2025)

## Abstract (arXiv)

Anomaly Detection (AD) on medical images enables a model to recognize any type of anomaly pattern without lesion-specific supervised learning. Data augmentation based methods construct pseudo-healthy images by "pasting" fake lesions on real healthy ones, and a network is trained to predict healthy images in a supervised manner. The lesion can be found by difference between the unhealthy input and pseudo-healthy output. However, using only manually designed fake lesions fail to approximate to irregular real lesions, hence limiting the model generalization. We assume by exploring the intrinsic data property within images, we can distinguish previously unseen lesions from healthy regions in an unhealthy image. In this study, we propose an Adaptive Fourier Space Compression (AFSC) module to distill healthy feature for AD. The compression of both magnitude and phase in frequency domain addresses the hyper intensity and diverse position of lesions. Experimental results on the BraTS and MS-SEG datasets demonstrate an AFSC baseline is able to produce promising detection results, and an AFSC module can be effectively embedded into existing AD methods.