Guided Reconstruction with Conditioned Diffusion Models for Unsupervised
Anomaly Detection in Brain MRIs
- URL: http://arxiv.org/abs/2312.04215v1
- Date: Thu, 7 Dec 2023 11:03:42 GMT
- Title: Guided Reconstruction with Conditioned Diffusion Models for Unsupervised
Anomaly Detection in Brain MRIs
- Authors: Finn Behrendt, Debayan Bhattacharya, Robin Mieling, Lennart Maack,
Julia Kr\"uger, Roland Opfer, Alexander Schlaefer
- Abstract summary: Diffusion models are an emerging class of deep generative models that show great potential regarding reconstruction fidelity.
We propose to condition the denoising mechanism of diffusion models with additional information about the image to reconstruct coming from a latent representation of the noise-free input image.
This conditioning enables high-fidelity reconstruction of healthy brain structures while aligning local intensity characteristics of input-reconstruction pairs.
- Score: 36.79912410985013
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Unsupervised anomaly detection in Brain MRIs aims to identify abnormalities
as outliers from a healthy training distribution. Reconstruction-based
approaches that use generative models to learn to reconstruct healthy brain
anatomy are commonly used for this task. Diffusion models are an emerging class
of deep generative models that show great potential regarding reconstruction
fidelity. However, they face challenges in preserving intensity characteristics
in the reconstructed images, limiting their performance in anomaly detection.
To address this challenge, we propose to condition the denoising mechanism of
diffusion models with additional information about the image to reconstruct
coming from a latent representation of the noise-free input image. This
conditioning enables high-fidelity reconstruction of healthy brain structures
while aligning local intensity characteristics of input-reconstruction pairs.
We evaluate our method's reconstruction quality, domain adaptation features and
finally segmentation performance on publicly available data sets with various
pathologies. Using our proposed conditioning mechanism we can reduce the
false-positive predictions and enable a more precise delineation of anomalies
which significantly enhances the anomaly detection performance compared to
established state-of-the-art approaches to unsupervised anomaly detection in
brain MRI. Furthermore, our approach shows promise in domain adaptation across
different MRI acquisitions and simulated contrasts, a crucial property of
general anomaly detection methods.
Related papers
- Synomaly Noise and Multi-Stage Diffusion: A Novel Approach for Unsupervised Anomaly Detection in Ultrasound Imaging [32.99597899937902]
We propose a novel unsupervised anomaly detection framework based on a diffusion model.
The proposed framework incorporates a synthetic anomaly (Synomaly) noise function and a multi-stage diffusion process.
We validate the proposed approach on carotid US, brain MRI, and liver CT datasets.
arXiv Detail & Related papers (2024-11-06T15:43:51Z) - On Instabilities of Unsupervised Denoising Diffusion Models in Magnetic Resonance Imaging Reconstruction [1.811105613701224]
Even tiny worst-case potential perturbations transferred from a surrogate model can cause these models to generate fake tissue structures.
The transferability of such worst-case perturbations indicates that the robustness of image reconstruction may be compromised.
arXiv Detail & Related papers (2024-06-23T19:44:00Z) - MAEDiff: Masked Autoencoder-enhanced Diffusion Models for Unsupervised
Anomaly Detection in Brain Images [40.89943932086941]
We propose a novel Masked Autoencoder-enhanced Diffusion Model (MAEDiff) for unsupervised anomaly detection in brain images.
The MAEDiff involves a hierarchical patch partition. It generates healthy images by overlapping upper-level patches and implements a mechanism based on the masked autoencoders operating on the sub-level patches to enhance the condition on the unnoised regions.
arXiv Detail & Related papers (2024-01-19T08:54:54Z) - DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection [55.48770333927732]
We propose a Difusion-based Anomaly Detection (DiAD) framework for multi-class anomaly detection.
It consists of a pixel-space autoencoder, a latent-space Semantic-Guided (SG) network with a connection to the stable diffusion's denoising network, and a feature-space pre-trained feature extractor.
Experiments on MVTec-AD and VisA datasets demonstrate the effectiveness of our approach.
arXiv Detail & Related papers (2023-12-11T18:38:28Z) - Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction [75.91471250967703]
We introduce a novel sampling framework called Steerable Conditional Diffusion.
This framework adapts the diffusion model, concurrently with image reconstruction, based solely on the information provided by the available measurement.
We achieve substantial enhancements in out-of-distribution performance across diverse imaging modalities.
arXiv Detail & Related papers (2023-08-28T08:47:06Z) - On Sensitivity and Robustness of Normalization Schemes to Input
Distribution Shifts in Automatic MR Image Diagnosis [58.634791552376235]
Deep Learning (DL) models have achieved state-of-the-art performance in diagnosing multiple diseases using reconstructed images as input.
DL models are sensitive to varying artifacts as it leads to changes in the input data distribution between the training and testing phases.
We propose to use other normalization techniques, such as Group Normalization and Layer Normalization, to inject robustness into model performance against varying image artifacts.
arXiv Detail & Related papers (2023-06-23T03:09:03Z) - Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI [55.78588835407174]
We propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy.
We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.
arXiv Detail & Related papers (2023-03-07T09:40:22Z) - The role of noise in denoising models for anomaly detection in medical
images [62.0532151156057]
Pathological brain lesions exhibit diverse appearance in brain images.
Unsupervised anomaly detection approaches have been proposed using only normal data for training.
We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes.
arXiv Detail & Related papers (2023-01-19T21:39:38Z) - Improved Slice-wise Tumour Detection in Brain MRIs by Computing
Dissimilarities between Latent Representations [68.8204255655161]
Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods.
We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder.
We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
arXiv Detail & Related papers (2020-07-24T14:02:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.