Region and Spatial Aware Anomaly Detection for Fundus Images

• URL: http://arxiv.org/abs/2303.03817v1
• Date: Tue, 7 Mar 2023 11:33:22 GMT
• Title: Region and Spatial Aware Anomaly Detection for Fundus Images
• Authors: Jingqi Niu, Shiwen Dong, Qinji Yu, Kang Dang and Xiaowei Ding
• Abstract summary: We propose a Region and Spatial Aware Anomaly Detection (ReSAD) method for fundus images. ReSAD obtains local region and long-range spatial information to reduce the false positives in the normal structure. Our method significantly outperforms the existing anomaly detection methods for fundus images on two publicly benchmark datasets.
• Score: 1.8374319565577157
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently anomaly detection has drawn much attention in diagnosing ocular diseases. Most existing anomaly detection research in fundus images has relatively large anomaly scores in the salient retinal structures, such as blood vessels, optical cups and discs. In this paper, we propose a Region and Spatial Aware Anomaly Detection (ReSAD) method for fundus images, which obtains local region and long-range spatial information to reduce the false positives in the normal structure. ReSAD transfers a pre-trained model to extract the features of normal fundus images and applies the Region-and-Spatial-Aware feature Combination module (ReSC) for pixel-level features to build a memory bank. In the testing phase, ReSAD uses the memory bank to determine out-of-distribution samples as abnormalities. Our method significantly outperforms the existing anomaly detection methods for fundus images on two publicly benchmark datasets.

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)
• Unsupervised Anomaly Detection via Masked Diffusion Posterior Sampling [8.887775968482208]
  diffusion models have shown promising applications for anomaly detection due to their powerful generative ability. This paper proposes a novel and highly-interpretable method named Masked Diffusion Posterior Sampling (MDPS) MDPS can achieve state-of-the-art performance in normal image reconstruction quality as well as anomaly detection and localization.
  arXiv  Detail & Related papers  (2024-04-27T13:13:27Z)
• 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)
• Weakly Supervised Anomaly Detection for Chest X-Ray Image [6.506869371228188]
  We propose a weakly supervised anomaly detection framework for Chest X-Ray (CXR) examination. WSCXR firstly constructs sets of normal and anomaly image features respectively. It then refines the anomaly image features by eliminating normal region features through anomaly feature mining.
  arXiv  Detail & Related papers  (2023-11-16T07:53:34Z)
• AnoDODE: Anomaly Detection with Diffusion ODE [0.0]
  Anomaly detection is the process of identifying atypical data samples that significantly deviate from the majority of the dataset. We propose a new anomaly detection method based on diffusion ODEs by estimating the density of features extracted from medical images. Our proposed method not only identifie anomalies but also provides interpretability at both the image and pixel levels.
  arXiv  Detail & Related papers  (2023-10-10T08:44:47Z)
• 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)
• Generative Residual Attention Network for Disease Detection [51.60842580044539]
  We present a novel approach for disease generation in X-rays using a conditional generative adversarial learning. We generate a corresponding radiology image in a target domain while preserving the identity of the patient. We then use the generated X-ray image in the target domain to augment our training to improve the detection performance.
  arXiv  Detail & Related papers  (2021-10-25T14:15:57Z)
• Contrastive Attention for Automatic Chest X-ray Report Generation [124.60087367316531]
  In most cases, the normal regions dominate the entire chest X-ray image, and the corresponding descriptions of these normal regions dominate the final report. We propose Contrastive Attention (CA) model, which compares the current input image with normal images to distill the contrastive information. We achieve the state-of-the-art results on the two public datasets.
  arXiv  Detail & Related papers  (2021-06-13T11:20:31Z)
• Modeling and Enhancing Low-quality Retinal Fundus Images [167.02325845822276]
  Low-quality fundus images increase uncertainty in clinical observation and lead to the risk of misdiagnosis. We propose a clinically oriented fundus enhancement network (cofe-Net) to suppress global degradation factors. Experiments on both synthetic and real images demonstrate that our algorithm effectively corrects low-quality fundus images without losing retinal details.
  arXiv  Detail & Related papers  (2020-05-12T08:01:16Z)

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.