Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Images

• URL: http://arxiv.org/abs/2008.03632v1
• Date: Sun, 9 Aug 2020 02:59:33 GMT
• Title: Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Images
• Authors: Kang Zhou, Yuting Xiao, Jianlong Yang, Jun Cheng, Wen Liu, Weixin Luo, Zaiwang Gu, Jiang Liu, Shenghua Gao
• Abstract summary: Anomaly detection in retinal image refers to the identification of abnormality caused by various retinal diseases/lesions. We propose to leverage the relation between the image texture and structure to design a deep neural network for anomaly detection.
• Score: 42.700275429734205
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Anomaly detection in retinal image refers to the identification of abnormality caused by various retinal diseases/lesions, by only leveraging normal images in training phase. Normal images from healthy subjects often have regular structures (e.g., the structured blood vessels in the fundus image, or structured anatomy in optical coherence tomography image). On the contrary, the diseases and lesions often destroy these structures. Motivated by this, we propose to leverage the relation between the image texture and structure to design a deep neural network for anomaly detection. Specifically, we first extract the structure of the retinal images, then we combine both the structure features and the last layer features extracted from original health image to reconstruct the original input healthy image. The image feature provides the texture information and guarantees the uniqueness of the image recovered from the structure. In the end, we further utilize the reconstructed image to extract the structure and measure the difference between structure extracted from original and the reconstructed image. On the one hand, minimizing the reconstruction difference behaves like a regularizer to guarantee that the image is corrected reconstructed. On the other hand, such structure difference can also be used as a metric for normality measurement. The whole network is termed as P-Net because it has a ``P'' shape. Extensive experiments on RESC dataset and iSee dataset validate the effectiveness of our approach for anomaly detection in retinal images. Further, our method also generalizes well to novel class discovery in retinal images and anomaly detection in real-world images.

Related papers

• Exploiting Structural Consistency of Chest Anatomy for Unsupervised Anomaly Detection in Radiography Images [20.80801110403552]
  We propose a Simple Space-Aware Memory Matrix for In-painting and Detecting anomalies from radiography images (abbreviated as SimSID) During the training, SimSID can taxonomize the anatomical structures into recurrent visual patterns, and in the inference, it can identify anomalies from the test image. Our SimSID surpasses the state of the arts in unsupervised anomaly detection by +8.0%, +5.0%, and +9.9% AUC scores on ZhangLab, COVIDx, and CheXpert benchmark datasets.
  arXiv  Detail & Related papers  (2024-03-13T16:44:49Z)
• MindDiffuser: Controlled Image Reconstruction from Human Brain Activity with Semantic and Structural Diffusion [8.299415606889024]
  We propose a two-stage image reconstruction model called MindDiffuser. In Stage 1, the VQ-VAE latent representations and the CLIP text embeddings decoded from fMRI are put into the image-to-image process of Stable Diffusion. In Stage 2, we utilize the low-level CLIP visual features decoded from fMRI as supervisory information.
  arXiv  Detail & Related papers  (2023-03-24T16:41:42Z)
• A Lightweight Reconstruction Network for Surface Defect Inspection [3.6823636353870954]
  This paper proposes an unsupervised defect detection algorithm based on a reconstruction network. The reconstruction network is designed through a fully convolutional autoencoder with a lightweight structure. The results show that the unsupervised defect detection algorithm of the reconstructed network has strong robustness and accuracy.
  arXiv  Detail & Related papers  (2022-12-25T08:59:15Z)
• Is Deep Image Prior in Need of a Good Education? [57.3399060347311]
  Deep image prior was introduced as an effective prior for image reconstruction. Despite its impressive reconstructive properties, the approach is slow when compared to learned or traditional reconstruction techniques. We develop a two-stage learning paradigm to address the computational challenge.
  arXiv  Detail & Related papers  (2021-11-23T15:08:26Z)
• Proxy-bridged Image Reconstruction Network for Anomaly Detection in Medical Images [59.700111685673846]
  Anomaly detection in medical images refers to the identification of abnormal images with only normal images in the training set. We propose a novel Proxy-bridged Image Reconstruction Network ( ProxyAno) for anomaly detection in medical images.
  arXiv  Detail & Related papers  (2021-10-05T00:40:43Z)
• Spatially-Adaptive Image Restoration using Distortion-Guided Networks [51.89245800461537]
  We present a learning-based solution for restoring images suffering from spatially-varying degradations. We propose SPAIR, a network design that harnesses distortion-localization information and dynamically adjusts to difficult regions in the image.
  arXiv  Detail & Related papers  (2021-08-19T11:02:25Z)
• On hallucinations in tomographic image reconstruction [3.486901561986149]
  An inaccurate prior might lead to false structures being hallucinated in the reconstructed image. Deep neural networks have been actively investigated for regularizing image reconstruction problems.
  arXiv  Detail & Related papers  (2020-12-01T17:14:28Z)
• Retinal Image Segmentation with a Structure-Texture Demixing Network [62.69128827622726]
  The complex structure and texture information are mixed in a retinal image, and distinguishing the information is difficult. Existing methods handle texture and structure jointly, which may lead biased models toward recognizing textures and thus results in inferior segmentation performance. We propose a segmentation strategy that seeks to separate structure and texture components and significantly improve the performance.
  arXiv  Detail & Related papers  (2020-07-15T12:19:03Z)
• Structural-analogy from a Single Image Pair [118.61885732829117]
  In this paper, we explore the capabilities of neural networks to understand image structure given only a single pair of images, A and B. We generate an image that keeps the appearance and style of B, but has a structural arrangement that corresponds to A. Our method can be used to generate high quality imagery in other conditional generation tasks utilizing images A and B only.
  arXiv  Detail & Related papers  (2020-04-05T14:51:10Z)

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.