Unsupervised 3D Brain Anomaly Detection

• URL: http://arxiv.org/abs/2010.04717v2
• Date: Fri, 9 Apr 2021 11:43:09 GMT
• Title: Unsupervised 3D Brain Anomaly Detection
• Authors: Jaime Simarro, Ezequiel de la Rosa, Thijs Vande Vyvere, David Robben and Diana M. Sima
• Abstract summary: Anomaly detection (AD) is the identification of data samples that do not fit a learned data distribution. Deep generative models, such as Generative Adrial Networks (GANs), can be exploited to capture anatomical variability. This study exemplifies the first AD approach that can efficiently handle volumetric data and detect 3D brain anomalies in one model.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Anomaly detection (AD) is the identification of data samples that do not fit a learned data distribution. As such, AD systems can help physicians to determine the presence, severity, and extension of a pathology. Deep generative models, such as Generative Adversarial Networks (GANs), can be exploited to capture anatomical variability. Consequently, any outlier (i.e., sample falling outside of the learned distribution) can be detected as an abnormality in an unsupervised fashion. By using this method, we can not only detect expected or known lesions, but we can even unveil previously unrecognized biomarkers. To the best of our knowledge, this study exemplifies the first AD approach that can efficiently handle volumetric data and detect 3D brain anomalies in one single model. Our proposal is a volumetric and high-detail extension of the 2D f-AnoGAN model obtained by combining a state-of-the-art 3D GAN with refinement training steps. In experiments using non-contrast computed tomography images from traumatic brain injury (TBI) patients, the model detects and localizes TBI abnormalities with an area under the ROC curve of ~75%. Moreover, we test the potential of the method for detecting other anomalies such as low quality images, preprocessing inaccuracies, artifacts, and even the presence of post-operative signs (such as a craniectomy or a brain shunt). The method has potential for rapidly labeling abnormalities in massive imaging datasets, as well as identifying new biomarkers.

Related papers

• Spatial-aware Attention Generative Adversarial Network for Semi-supervised Anomaly Detection in Medical Image [63.59114880750643]
  We introduce a novel Spatial-aware Attention Generative Adrialversa Network (SAGAN) for one-class semi-supervised generation of health images. SAGAN generates high-quality health images corresponding to unlabeled data, guided by the reconstruction of normal images and restoration of pseudo-anomaly images. Extensive experiments on three medical datasets demonstrate that the proposed SAGAN outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-05-21T15:41:34Z)
• 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)
• Learning image representations for anomaly detection: application to discovery of histological alterations in drug development [0.0]
  In histology, normal samples are usually abundant, whereas anomalous (pathological) cases are scarce or not available. Such approaches combined with pre-trained Convolutional Neural Network (CNN) representations of images were previously employed for anomaly detection (AD) We show that our approach can be used for toxicity assessment of candidate drugs at early development stages and thereby may reduce expensive late-stage drug attrition.
  arXiv  Detail & Related papers  (2022-10-14T10:00:36Z)
• 3D unsupervised anomaly detection and localization through virtual multi-view projection and reconstruction: Clinical validation on low-dose chest computed tomography [2.2302915692528367]
  We propose a method based on a deep neural network for computer-aided diagnosis called virtual multi-view projection and reconstruction. The proposed method improves the patient-level anomaly detection by 10% compared with a gold standard based on supervised learning. It localizes the anomaly region with 93% accuracy, demonstrating its high performance.
  arXiv  Detail & Related papers  (2022-06-18T13:22:00Z)
• StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational Autoencoder [48.2010192865749]
  Unsupervised anomaly detection (UAD) can learn a data distribution from an unlabelled dataset of healthy subjects and then be applied to detect out of distribution samples. This research proposes a compact version of the "context-encoding" VAE (ceVAE) model, combined with pre and post-processing steps, creating a UAD pipeline (StRegA) The proposed pipeline achieved a Dice score of 0.642$pm$0.101 while detecting tumours in T2w images of the BraTS dataset and 0.859$pm$0.112 while detecting artificially induced anomalies.
  arXiv  Detail & Related papers  (2022-01-31T14:27:35Z)
• SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection [76.01333073259677]
  We propose the use of Space-aware Memory Queues for In-painting and Detecting anomalies from radiography images (abbreviated as SQUID) We show that SQUID can taxonomize the ingrained anatomical structures into recurrent patterns; and in the inference, it can identify anomalies (unseen/modified patterns) in the image.
  arXiv  Detail & Related papers  (2021-11-26T13:47:34Z)
• Explainable multiple abnormality classification of chest CT volumes with AxialNet and HiResCAM [89.2175350956813]
  We introduce the challenging new task of explainable multiple abnormality classification in volumetric medical images. We propose a multiple instance learning convolutional neural network, AxialNet, that allows identification of top slices for each abnormality. We then aim to improve the model's learning through a novel mask loss that leverages HiResCAM and 3D allowed regions.
  arXiv  Detail & Related papers  (2021-11-24T01:14:33Z)
• Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images [23.79184121052212]
  Unsupervised anomaly detection (UAD) learns one-class classifiers exclusively with normal (i.e., healthy) images. We propose a novel self-supervised representation learning method, called Constrained Contrastive Distribution learning for anomaly detection (CCD) Our method outperforms current state-of-the-art UAD approaches on three different colonoscopy and fundus screening datasets.
  arXiv  Detail & Related papers  (2021-03-05T01:56:58Z)
• Brain Tumor Anomaly Detection via Latent Regularized Adversarial Network [34.81845999071626]
  We propose an innovative brain tumor abnormality detection algorithm. The semi-supervised anomaly detection model is proposed in which only healthy (normal) brain images are trained.
  arXiv  Detail & Related papers  (2020-07-09T12:12:16Z)

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.