mlsanityData to 14 Oct 2026

REFLECT: Rectified Flows for Efficient Brain Anomaly Correction Transport

arXiv 2508.02889 ↗First posted 4 Aug 2025eess.IV cs.CVcited by 6

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Unsupervised anomaly detection (UAD) in brain imaging is crucial for identifying pathologies without the need for labeled data. However, accurately localizing anomalies remains challenging due to the intricate structure of brain anatomy and the scarcity of abnormal examples. In this work, we introduce REFLECT, a novel framework that leverages rectified flows to establish a direct, linear trajectory for correcting abnormal MR images toward a normal distribution. By learning a straight, one-step correction transport map, our method efficiently corrects brain anomalies and can precisely localize anomalies by detecting discrepancies between anomalous input and corrected counterpart. In contrast to the diffusion-based UAD models, which require iterative stochastic sampling, rectified flows provide a direct transport map, enabling single-step inference. Extensive experiments on popular UAD brain segmentation benchmarks demonstrate that REFLECT significantly outperforms state-of-the-art unsupervised anomaly detection methods. The code is available at https://github.com/farzad-bz/REFLECT.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 26 of its 36 references that are in the map. First posted on arXiv 4 Aug 2025.

Builds on

26 of 36 references.

Closest ancestors · 6

Same direction, newest first

  1. 2025MAD-AD: Masked Diffusion for Unsupervised Brain Anomaly Detection24 Feb
  2. 2024Diffusion Models with Implicit Guidance for Medical Anomaly Detection13 Mar
  3. 2023DISYRE: Diffusion-Inspired SYnthetic REstoration for Unsupervised Anomaly Detection26 Nov
  4. 2023Modality Cycles with Masked Conditional Diffusion for Unsupervised Anomaly Segmentation in MRI30 Aug
  5. 2023Mask, Stitch, and Re-Sample: Enhancing Robustness and Generalizability in Anomaly Detection through Automatic Diffusion Models31 May
  6. 2023Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI7 Mar

Other references · 20

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  3. 2020Denoising Diffusion Implicit Modelscited by 9,781
  4. 2022Generative Adversarial Networkscited by 5,627
  5. 2024Scaling Rectified Flow Transformers for High-Resolution Image Synthesiscited by 4,181
  6. 2018Neural Ordinary Differential Equationscited by 3,779
  7. 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flowcited by 3,700
  8. 2015Variational Inference with Normalizing Flowscited by 2,170
  9. 2013Describing Textures in the Wildcited by 2,125
  10. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  11. 2021CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flowscited by 321
  12. 2023Understanding and Mitigating Copying in Diffusion Modelscited by 209
8 more
  1. 2020Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Studycited by 147
  2. 2018Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Imagescited by 146
  3. 2019Unsupervised Anomaly Localization using Variational Auto-Encoderscited by 73
  4. 2020Detecting Outliers with Foreign Patch Interpolationcited by 44
  5. 2021Detecting Outliers with Poisson Image Interpolationcited by 44
  6. 2022Constrained unsupervised anomaly segmentationcited by 19
  7. 2024Leveraging the Mahalanobis Distance to enhance Unsupervised Brain MRI Anomaly Detectioncited by 10
  8. 2024IterMask2: Iterative Unsupervised Anomaly Segmentation via Spatial and Frequency Masking for Brain Lesions in MRIcited by 9

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 4.

6 papers.

  1. 2026Zero-Flow Encoderscited by 4
  2. 2026Flow Mismatching: Unsupervised Anomaly Detection via Velocity Discrepancies in Flow Matching Modelscited by 2
  3. 2025Unsupervised Deep Generative Models for Anomaly Detection in Neuroimaging: A Systematic Scoping Reviewcited by 1
  4. 2025Unsupervised Detection of Post-Stroke Brain Abnormalitiescited by 0
  5. 2026MIRTO: a registration-gated, multiverse-tested evaluation protocol for unsupervised anomaly segmentation in brain MRIcited by 0
  6. 2026Mitigating Convergence Collapse in Fixed-Target Anomaly Detectors via Kernel-Anchored Locality Regularizationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Unsupervised Brain Anomaly Detection as a Bayesian Inverse Problem with Diffusion Prior
  2. 2023Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIs
  3. 2025Conditional diffusion models for guided anomaly detection in brain images using fluid-driven anomaly randomization
  4. 2024Synomaly Noise and Multi-Stage Diffusion: A Novel Approach for Unsupervised Anomaly Detection in Medical Images
  5. 2023Unsupervised Anomaly Detection using Aggregated Normative Diffusion
  6. 2024Unsupervised contrastive analysis for anomaly detection in brain MRIs via conditional diffusion models
  7. 2026Reconstruction-Shift Discrimination via Mask-Guided Latent Diffusion for Medical Anomaly Detection
  8. 2024AnoFPDM: Anomaly Segmentation with Forward Process of Diffusion Models for Brain MRI