mlsanityData to 14 Oct 2026

Histogram- and Diffusion-Based Medical Out-of-Distribution Detection

arXiv 2310.08654 ↗First posted 12 Oct 2023cs.CVcited by 2

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Out-of-distribution (OOD) detection is crucial for the safety and reliability of artificial intelligence algorithms, especially in the medical domain. In the context of the Medical OOD (MOOD) detection challenge 2023, we propose a pipeline that combines a histogram-based method and a diffusion-based method. The histogram-based method is designed to accurately detect homogeneous anomalies in the toy examples of the challenge, such as blobs with constant intensity values. The diffusion-based method is based on one of the latest methods for unsupervised anomaly detection, called DDPM-OOD. We explore this method and propose extensive post-processing steps for pixel-level and sample-level anomaly detection on brain MRI and abdominal CT data provided by the challenge. Our results show that the proposed DDPM method is sensitive to blur and bias field samples, but faces challenges with anatomical deformation, black slice, and swapped patches. These findings suggest that further research is needed to improve the performance of DDPM for OOD detection in medical images.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 15 of its 18 references that are in the map. First posted on arXiv 12 Oct 2023.

Builds on

15 of 18 references.

Closest ancestors · 5

Same direction, newest first

  1. 2023Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI7 Mar
  2. 2022Denoising diffusion models for out-of-distribution detection14 Nov
  3. 2022Brain Imaging Generation with Latent Diffusion Models15 Sep
  4. 2022Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models7 Jun
  5. 2022Diffusion Models for Medical Anomaly Detection8 Mar

Other references · 10

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  3. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  4. 2022Diffusion Models in Vision: A Surveycited by 1,015
  5. 2021Generalized Out-of-Distribution Detection: A Surveycited by 802
  6. 2018Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoderscited by 83
  7. 2023Generative AI for Medical Imaging: extending the MONAI Frameworkcited by 68
  8. 2018Context-encoding Variational Autoencoder for Unsupervised Anomaly Detectioncited by 64
  9. 2020Anomaly detection through latent space restoration using vector-quantized variational autoencoderscited by 45
  10. 2020DeScarGAN: Disease-Specific Anomaly Detection with Weak Supervisioncited by 13

Built on by

Cited by year

Citing papers per year: 2024: 2.

2 papers.

  1. 2024Binary Noise for Binary Tasks: Masked Bernoulli Diffusion for Unsupervised Anomaly Detectioncited by 7
  2. 2024Enhancing Reconstruction-Based Out-of-Distribution Detection in Brain MRI with Model and Metric Ensemblescited by 4

Similar, not cited

Close in wording, no citation either way

  1. 2025Out-of-Distribution Detection in Medical Imaging via Diffusion Trajectories
  2. 2024NODI: Out-Of-Distribution Detection with Noise from Diffusion
  3. 2023Dual Conditioned Diffusion Models for Out-Of-Distribution Detection: Application to Fetal Ultrasound Videos
  4. 2025Delving into Out-of-Distribution Detection with Medical Vision-Language Models
  5. 2023Unsupervised 3D out-of-distribution detection with latent diffusion models
  6. 2024Exploiting Diffusion Prior for Out-of-Distribution Detection
  7. 2024Out-of-Distribution Detection with a Single Unconditional Diffusion Model
  8. 2026Geometry over Density: Few-Shot Cross-Domain OOD Detection