The role of noise in denoising models for anomaly detection in medical images

• URL: http://arxiv.org/abs/2301.08330v1
• Date: Thu, 19 Jan 2023 21:39:38 GMT
• Title: The role of noise in denoising models for anomaly detection in medical images
• Authors: Antanas Kascenas, Pedro Sanchez, Patrick Schrempf, Chaoyang Wang, William Clackett, Shadia S. Mikhael, Jeremy P. Voisey, Keith Goatman, Alexander Weir, Nicolas Pugeault, Sotirios A. Tsaftaris, Alison Q. O'Neil
• Abstract summary: 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.
• Score: 62.0532151156057
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pathological brain lesions exhibit diverse appearance in brain images, in terms of intensity, texture, shape, size, and location. Comprehensive sets of data and annotations are difficult to acquire. Therefore, unsupervised anomaly detection approaches have been proposed using only normal data for training, with the aim of detecting outlier anomalous voxels at test time. Denoising methods, for instance classical denoising autoencoders (DAEs) and more recently emerging diffusion models, are a promising approach, however naive application of pixelwise noise leads to poor anomaly detection performance. We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes, with similar noise parameter adjustments giving good performance for both DAEs and diffusion models. Visual inspection of the reconstructions suggests that the training noise influences the trade-off between the extent of the detail that is reconstructed and the extent of erasure of anomalies, both of which contribute to better anomaly detection performance. We validate our findings on two real-world datasets (tumor detection in brain MRI and hemorrhage/ischemia/tumor detection in brain CT), showing good detection on diverse anomaly appearances. Overall, we find that a DAE trained with coarse noise is a fast and simple method that gives state-of-the-art accuracy. Diffusion models applied to anomaly detection are as yet in their infancy and provide a promising avenue for further research.

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)
• GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detection [60.78684630040313]
  Diffusion models tend to reconstruct normal counterparts of test images with certain noises added. From the global perspective, the difficulty of reconstructing images with different anomalies is uneven. We propose a global and local adaptive diffusion model (abbreviated to GLAD) for unsupervised anomaly detection.
  arXiv  Detail & Related papers  (2024-06-11T17:27:23Z)
• AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model [59.08735812631131]
  Anomaly inspection plays an important role in industrial manufacture. Existing anomaly inspection methods are limited in their performance due to insufficient anomaly data. We propose AnomalyDiffusion, a novel diffusion-based few-shot anomaly generation model.
  arXiv  Detail & Related papers  (2023-12-10T05:13:40Z)
• Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIs [36.79912410985013]
  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.
  arXiv  Detail & Related papers  (2023-12-07T11:03:42Z)
• Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach [49.995833831087175]
  This work proposes a novel method for generating generic Video-temporal PAs by inpainting a masked out region of an image. In addition, we present a simple unified framework to detect real-world anomalies under the OCC setting. Our method performs on par with other existing state-of-the-art PAs generation and reconstruction based methods under the OCC setting.
  arXiv  Detail & Related papers  (2023-11-27T13:14:06Z)
• 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)
• Deep Ultrasound Denoising Using Diffusion Probabilistic Models [5.828784149537374]
  Previous denoising methods often remove the speckles, which could be informative for radiologists and also for quantitative ultrasound. Herein, a method based on the recent Denoising Diffusion Probabilistic Models (DDPM) is proposed. It iteratively enhances the image quality by eliminating the noise while preserving the speckle texture.
  arXiv  Detail & Related papers  (2023-06-12T21:53:32Z)
• Improving the Robustness of Summarization Models by Detecting and Removing Input Noise [50.27105057899601]
  We present a large empirical study quantifying the sometimes severe loss in performance from different types of input noise for a range of datasets and model sizes. We propose a light-weight method for detecting and removing such noise in the input during model inference without requiring any training, auxiliary models, or even prior knowledge of the type of noise.
  arXiv  Detail & Related papers  (2022-12-20T00:33:11Z)
• MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion [38.62448918459113]
  We propose a new denoising method based on score-based reverse diffusion sampling. Our network, trained only with coronal knee scans, excels even on out-of-distribution in vivo liver MRI data.
  arXiv  Detail & Related papers  (2022-03-23T10:35:06Z)
• Diffusion Models for Medical Anomaly Detection [0.8999666725996974]
  We present a novel weakly supervised anomaly detection method based on denoising diffusion implicit models. Our method generates very detailed anomaly maps without the need for a complex training procedure.
  arXiv  Detail & Related papers  (2022-03-08T12:35:07Z)

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.