Evaluation of Contemporary Convolutional Neural Network Architectures for Detecting COVID-19 from Chest Radiographs

• URL: http://arxiv.org/abs/2007.01108v1
• Date: Tue, 30 Jun 2020 15:22:39 GMT
• Title: Evaluation of Contemporary Convolutional Neural Network Architectures for Detecting COVID-19 from Chest Radiographs
• Authors: Nikita Albert
• Abstract summary: We train and evaluate three model architectures, proposed for chest radiograph analysis, under varying conditions. We find issues that discount the impressive model performances proposed by contemporary studies on this subject.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Interpreting chest radiograph, a.ka. chest x-ray, images is a necessary and crucial diagnostic tool used by medical professionals to detect and identify many diseases that may plague a patient. Although the images themselves contain a wealth of valuable information, their usefulness may be limited by how well they are interpreted, especially when the reviewing radiologist may be fatigued or when or an experienced radiologist is unavailable. Research in the use of deep learning models to analyze chest radiographs yielded impressive results where, in some instances, the models outperformed practicing radiologists. Amidst the COVID-19 pandemic, researchers have explored and proposed the use of said deep models to detect COVID-19 infections from radiographs as a possible way to help ease the strain on medical resources. In this study, we train and evaluate three model architectures, proposed for chest radiograph analysis, under varying conditions, find issues that discount the impressive model performances proposed by contemporary studies on this subject, and propose methodologies to train models that yield more reliable results.. Code, scripts, pre-trained models, and visualizations are available at https://github.com/nalbert/COVID-detection-from-radiographs.

Related papers

• UMedNeRF: Uncertainty-aware Single View Volumetric Rendering for Medical Neural Radiance Fields [38.62191342903111]
  We propose an Uncertainty-aware MedNeRF (UMedNeRF) network based on generated radiation fields. We show the results of CT projection rendering with a single X-ray and compare our method with other methods based on generated radiation fields.
  arXiv  Detail & Related papers  (2023-11-10T02:47:15Z)
• XrayGPT: Chest Radiographs Summarization using Medical Vision-Language Models [60.437091462613544]
  We introduce XrayGPT, a novel conversational medical vision-language model. It can analyze and answer open-ended questions about chest radiographs. We generate 217k interactive and high-quality summaries from free-text radiology reports.
  arXiv  Detail & Related papers  (2023-06-13T17:59:59Z)
• Act Like a Radiologist: Radiology Report Generation across Anatomical Regions [50.13206214694885]
  X-RGen is a radiologist-minded report generation framework across six anatomical regions. In X-RGen, we seek to mimic the behaviour of human radiologists, breaking them down into four principal phases. We enhance the recognition capacity of the image encoder by analysing images and reports across various regions.
  arXiv  Detail & Related papers  (2023-05-26T07:12:35Z)
• Improving Chest X-Ray Classification by RNN-based Patient Monitoring [0.34998703934432673]
  We analyze how information about diagnosis can improve CNN-based image classification models. We show that a model trained on additional patient history information outperforms a model trained without the information by a significant margin.
  arXiv  Detail & Related papers  (2022-10-28T11:47:15Z)
• Medical Image Captioning via Generative Pretrained Transformers [57.308920993032274]
  We combine two language models, the Show-Attend-Tell and the GPT-3, to generate comprehensive and descriptive radiology records. The proposed model is tested on two medical datasets, the Open-I, MIMIC-CXR, and the general-purpose MS-COCO.
  arXiv  Detail & Related papers  (2022-09-28T10:27:10Z)
• MedNeRF: Medical Neural Radiance Fields for Reconstructing 3D-aware CT-Projections from a Single X-ray [14.10611608681131]
  Excessive ionising radiation can lead to deterministic and harmful effects on the body. This paper proposes a Deep Learning model that learns to reconstruct CT projections from a few or even a single-view X-ray.
  arXiv  Detail & Related papers  (2022-02-02T13:25:23Z)
• 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)
• 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)
• Computer-aided abnormality detection in chest radiographs in a clinical setting via domain-adaptation [0.23624125155742057]
  Deep learning (DL) models are being deployed at medical centers to aid radiologists for diagnosis of lung conditions from chest radiographs. These pre-trained DL models' ability to generalize in clinical settings is poor because of the changes in data distributions between publicly available and privately held radiographs. In this work, we introduce a domain-shift detection and removal method to overcome this problem.
  arXiv  Detail & Related papers  (2020-12-19T01:01:48Z)
• XraySyn: Realistic View Synthesis From a Single Radiograph Through CT Priors [118.27130593216096]
  A radiograph visualizes the internal anatomy of a patient through the use of X-ray, which projects 3D information onto a 2D plane. To the best of our knowledge, this is the first work on radiograph view synthesis. We show that by gaining an understanding of radiography in 3D space, our method can be applied to radiograph bone extraction and suppression without groundtruth bone labels.
  arXiv  Detail & Related papers  (2020-12-04T05:08:53Z)
• Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment [39.60171837961607]
  We develop a neural network model that is trained on both images and free-text to assess pulmonary edema severity from chest radiographs at inference time. Our experimental results suggest that the joint image-text representation learning improves the performance of pulmonary edema assessment.
  arXiv  Detail & Related papers  (2020-08-22T17:28:39Z)

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.