A versatile anomaly detection method for medical images with a flow-based generative model in semi-supervision setting

• URL: http://arxiv.org/abs/2001.07847v3
• Date: Tue, 20 Oct 2020 07:09:03 GMT
• Title: A versatile anomaly detection method for medical images with a flow-based generative model in semi-supervision setting
• Authors: H. Shibata (1), S. Hanaoka (2), Y. Nomura (1), T. Nakao (1), I. Sato (2 and 4 and 5), D. Sato (3), N. Hayashi (1) and O. Abe (2 and 3) ((1) Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, (2) Department of Radiology, The University of Tokyo Hospital, (3) Division of Radiology and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, (4) Department of Computer Science, Graduate School of Information Science and Technology, The University of Tokyo, (5) Center for Advanced Intelligence Project, RIKEN)
• Abstract summary: We present an anomaly detection method based on two trained flow-based generative models. With this method, the posterior probability can be computed as a normality metric for any given image. The method was validated with two types of medical images: chest X-ray radiographs (CXRs) and brain computed tomographies (BCTs)
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Oversight in medical images is a crucial problem, and timely reporting of medical images is desired. Therefore, an all-purpose anomaly detection method that can detect virtually all types of lesions/diseases in a given image is strongly desired. However, few commercially available and versatile anomaly detection methods for medical images have been provided so far. Recently, anomaly detection methods built upon deep learning methods have been rapidly growing in popularity, and these methods seem to provide reasonable solutions to the problem. However, the workload to label the images necessary for training in deep learning remains heavy. In this study, we present an anomaly detection method based on two trained flow-based generative models. With this method, the posterior probability can be computed as a normality metric for any given image. The training of the generative models requires two sets of images: a set containing only normal images and another set containing both normal and abnormal images without any labels. In the latter set, each sample does not have to be labeled as normal or abnormal; therefore, any mixture of images (e.g., all cases in a hospital) can be used as the dataset without cumbersome manual labeling. The method was validated with two types of medical images: chest X-ray radiographs (CXRs) and brain computed tomographies (BCTs). The areas under the receiver operating characteristic curves for logarithm posterior probabilities of CXRs (0.868 for pneumonia-like opacities) and BCTs (0.904 for infarction) were comparable to those in previous studies with other anomaly detection methods. This result showed the versatility of our method.

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)
• MedIAnomaly: A comparative study of anomaly detection in medical images [26.319602363581442]
  Anomaly detection (AD) aims at detecting abnormal samples that deviate from the expected normal patterns. Despite numerous methods for medical AD, we observe a lack of a fair and comprehensive evaluation. This paper builds a benchmark with unified comparison.
  arXiv  Detail & Related papers  (2024-04-06T06:18:11Z)
• Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images [68.42215385041114]
  This paper introduces a novel lightweight multi-level adaptation and comparison framework to repurpose the CLIP model for medical anomaly detection. Our approach integrates multiple residual adapters into the pre-trained visual encoder, enabling a stepwise enhancement of visual features across different levels. Our experiments on medical anomaly detection benchmarks demonstrate that our method significantly surpasses current state-of-the-art models.
  arXiv  Detail & Related papers  (2024-03-19T09:28:19Z)
• Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Images [14.191760807405421]
  Prior anomaly detection methods were optimized for a specific "known" abnormality. In this study, we compare various anomaly detection methods across four medical datasets. We propose a simple decision-level ensemble method to leverage the advantage of different kinds of anomaly detection without knowing the abnormality.
  arXiv  Detail & Related papers  (2023-07-03T04:56:17Z)
• Stain-invariant self supervised learning for histopathology image analysis [74.98663573628743]
  We present a self-supervised algorithm for several classification tasks within hematoxylin and eosin stained images of breast cancer. Our method achieves the state-of-the-art performance on several publicly available breast cancer datasets.
  arXiv  Detail & Related papers  (2022-11-14T18:16:36Z)
• Dual-Distribution Discrepancy for Anomaly Detection in Chest X-Rays [29.57501199670898]
  We propose a novel strategy, Dual-distribution Discrepancy for Anomaly Detection (DDAD), utilizing both known normal images and unlabeled images. Experiments on three CXR datasets demonstrate that the proposed DDAD achieves consistent, significant gains and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-06-08T14:52:27Z)
• PatchNR: Learning from Small Data by Patch Normalizing Flow Regularization [57.37911115888587]
  We introduce a regularizer for the variational modeling of inverse problems in imaging based on normalizing flows. Our regularizer, called patchNR, involves a normalizing flow learned on patches of very few images.
  arXiv  Detail & Related papers  (2022-05-24T12:14:26Z)
• 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)
• Generative Adversarial U-Net for Domain-free Medical Image Augmentation [49.72048151146307]
  The shortage of annotated medical images is one of the biggest challenges in the field of medical image computing. In this paper, we develop a novel generative method named generative adversarial U-Net. Our newly designed model is domain-free and generalizable to various medical images.
  arXiv  Detail & Related papers  (2021-01-12T23:02:26Z)
• Convolutional-LSTM for Multi-Image to Single Output Medical Prediction [55.41644538483948]
  A common scenario in developing countries is to have the volume metadata lost due multiple reasons. It is possible to get a multi-image to single diagnostic model which mimics human doctor diagnostic process.
  arXiv  Detail & Related papers  (2020-10-20T04:30:09Z)
• Anomaly Detection in Medical Imaging with Deep Perceptual Autoencoders [1.7277957019593995]
  We introduce a new powerful method of image anomaly detection. It relies on the classical autoencoder approach with a re-designed training pipeline. It outperforms state-of-the-art approaches in complex medical image analysis tasks.
  arXiv  Detail & Related papers  (2020-06-23T18:45:55Z)

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.