AdverX-Ray: Ensuring X-Ray Integrity Through Frequency-Sensitive Adversarial VAEs

• URL: http://arxiv.org/abs/2502.16610v1
• Date: Sun, 23 Feb 2025 15:32:40 GMT
• Title: AdverX-Ray: Ensuring X-Ray Integrity Through Frequency-Sensitive Adversarial VAEs
• Authors: Francisco Caetano, Christiaan Viviers, Lena Filatova, Peter H. N. de With, Fons van der Sommen,
• Abstract summary: AdverX-Ray serves as an image-quality assessment layer.<n>It is trained on patches from X-ray images of specific machine models.<n>It can evaluate whether a scan matches the training distribution, or if a scan from the same machine is captured under different settings.
• Score: 7.0477485974331895
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ensuring the quality and integrity of medical images is crucial for maintaining diagnostic accuracy in deep learning-based Computer-Aided Diagnosis and Computer-Aided Detection (CAD) systems. Covariate shifts are subtle variations in the data distribution caused by different imaging devices or settings and can severely degrade model performance, similar to the effects of adversarial attacks. Therefore, it is vital to have a lightweight and fast method to assess the quality of these images prior to using CAD models. AdverX-Ray addresses this need by serving as an image-quality assessment layer, designed to detect covariate shifts effectively. This Adversarial Variational Autoencoder prioritizes the discriminator's role, using the suboptimal outputs of the generator as negative samples to fine-tune the discriminator's ability to identify high-frequency artifacts. Images generated by adversarial networks often exhibit severe high-frequency artifacts, guiding the discriminator to focus excessively on these components. This makes the discriminator ideal for this approach. Trained on patches from X-ray images of specific machine models, AdverX-Ray can evaluate whether a scan matches the training distribution, or if a scan from the same machine is captured under different settings. Extensive comparisons with various OOD detection methods show that AdverX-Ray significantly outperforms existing techniques, achieving a 96.2% average AUROC using only 64 random patches from an X-ray. Its lightweight and fast architecture makes it suitable for real-time applications, enhancing the reliability of medical imaging systems. The code and pretrained models are publicly available.

Related papers

• Generative AI: A Pix2pix-GAN-Based Machine Learning Approach for Robust and Efficient Lung Segmentation [0.7614628596146602]
  This study develops a deep learning framework using a Pix2pix Generative Adversarial Network (GAN) to segment pulmonary abnormalities from CXR images.<n>The framework's image preprocessing and augmentation techniques were properly incorporated with a U-Net-inspired generator-discriminator architecture.
  arXiv  Detail & Related papers  (2024-12-14T13:12:09Z)
• Enhancing Prohibited Item Detection through X-ray-Specific Augmentation and Contextual Feature Integration [81.11400642272976]
  X-ray prohibited item detection faces challenges due to the long-tail distribution and unique characteristics of X-ray imaging. Traditional data augmentation strategies, such as copy-paste and mixup, are ineffective at improving the detection of rare items. We propose the X-ray Imaging-driven Detection Network (XIDNet) to address these challenges.
  arXiv  Detail & Related papers  (2024-11-27T06:13:56Z)
• StealthDiffusion: Towards Evading Diffusion Forensic Detection through Diffusion Model [62.25424831998405]
  StealthDiffusion is a framework that modifies AI-generated images into high-quality, imperceptible adversarial examples. It is effective in both white-box and black-box settings, transforming AI-generated images into high-quality adversarial forgeries.
  arXiv  Detail & Related papers  (2024-08-11T01:22:29Z)
• 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)
• Quantifying the effect of X-ray scattering for data generation in real-time defect detection [1.124958340749622]
  In-line detection requires highly accurate, robust, and fast algorithms. DCNNs satisfy these requirements when a large amount of labeled data is available. X-ray scattering is known to be computationally expensive to simulate.
  arXiv  Detail & Related papers  (2023-05-22T08:29:43Z)
• Artificial Intelligence for Automatic Detection and Classification Disease on the X-Ray Images [0.0]
  This work presents rapid detection of diseases in the lung using the efficient Deep learning pre-trained RepVGG algorithm. We are applying Artificial Intelligence technology for automatic highlighted detection of affected areas of people's lungs.
  arXiv  Detail & Related papers  (2022-11-14T03:51:12Z)
• Preservation of High Frequency Content for Deep Learning-Based Medical Image Classification [74.84221280249876]
  An efficient analysis of large amounts of chest radiographs can aid physicians and radiologists. We propose a novel Discrete Wavelet Transform (DWT)-based method for the efficient identification and encoding of visual information.
  arXiv  Detail & Related papers  (2022-05-08T15:29:54Z)
• 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)
• On the impact of using X-ray energy response imagery for object detection via Convolutional Neural Networks [17.639472693362926]
  We study the impact of variant X-ray imagery, i.e. X-ray energy response (high, low) and effective-z compared to geometries. We evaluate CNN architectures to explore the transferability of models trained with such 'raw' variant imagery.
  arXiv  Detail & Related papers  (2021-08-27T21:28:28Z)
• Cross-Modal Contrastive Learning for Abnormality Classification and Localization in Chest X-rays with Radiomics using a Feedback Loop [63.81818077092879]
  We propose an end-to-end semi-supervised cross-modal contrastive learning framework for medical images. We first apply an image encoder to classify the chest X-rays and to generate the image features. The radiomic features are then passed through another dedicated encoder to act as the positive sample for the image features generated from the same chest X-ray.
  arXiv  Detail & Related papers  (2021-04-11T09:16:29Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)

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.