mlsanityData to 14 Oct 2026

AFSC: Adaptive Fourier Space Compression for Anomaly Detection

arXiv 2204.07963 ↗First posted 17 Apr 2022eess.IV cs.CVcited by 12

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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.

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

14 of 42 references.

References · 14

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2022Generative Adversarial Networkscited by 5,627
  3. 2017Squeeze-and-Excitation Networkscited by 4,789
  4. 2018TSM: Temporal Shift Module for Efficient Video Understandingcited by 856
  5. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 776
  6. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  7. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 514
  8. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 511
  9. 2019Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddingscited by 386
  10. 2020Multiresolution Knowledge Distillation for Anomaly Detectioncited by 285
  11. 2021The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localizationcited by 178
  12. 2020Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Studycited by 147
2 more
  1. 2022Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detectioncited by 93
  2. 2021ASC-Net : Adversarial-based Selective Network for Unsupervised Anomaly Segmentationcited by 15

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 8; 2025: 3; 2026: 1.

12 papers.

  1. 2024U-KAN Makes Strong Backbone for Medical Image Segmentation and Generationcited by 179
  2. 2024Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Imagescited by 69
  3. 2024Endora: Video Generation Models as Endoscopy Simulatorscited by 55
  4. 2024LGS: A Light-weight 4D Gaussian Splatting for Efficient Surgical Scene Reconstructioncited by 21
  5. 2024EndoSparse: Real-Time Sparse View Synthesis of Endoscopic Scenes using Gaussian Splattingcited by 16
  6. 2024GaussianStego: A Generalizable Stenography Pipeline for Generative 3D Gaussians Splattingcited by 11
  7. 2024GTP-4o: Modality-prompted Heterogeneous Graph Learning for Omni-modal Biomedical Representationcited by 7
  8. 2024When 3D Partial Points Meets SAM: Tooth Point Cloud Segmentation with Sparse Labelscited by 5
  9. 2025Exploiting Point-Language Models with Dual-Prompts for 3D Anomaly Detectioncited by 4
  10. 2025A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospectscited by 2
  11. 2026Beyond Sparse Supervision: Diffusion-Guided Learning for Few-Shot Graph Fraud Detectioncited by 2
  12. 2025SD-MAD: Sign-Driven Few-shot Multi-Anomaly Detection in Medical Imagescited by 0