mlsanityData to 7 Oct 2026

CXR-Net: An Artificial Intelligence Pipeline for Quick Covid-19 Screening of Chest X-Rays

arXiv 2103.00087 ↗First posted 26 Feb 2021eess.IV cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

CXR-Net is a two-module Artificial Intelligence pipeline for the quick detection of SARS-CoV-2 from chest X-rays (CXRs). Module 1 was trained on a public dataset of 6395 CXRs with radiologist annotated lung contours to generate masks of the lungs that overlap the heart and large vasa. Module 2 is a hybrid convnet in which the first convolutional layer with learned coefficients is replaced by a layer with fixed coefficients provided by the Wavelet Scattering Transform (WST). Module 2 takes as inputs the patients CXRs and corresponding lung masks calculated by Module 1, and produces as outputs a class assignment (Covid vs. non-Covid) and high resolution heat maps that identify the SARS associated lung regions. Module 2 was trained on a dataset of CXRs from non-Covid and RT-PCR confirmed Covid patients acquired at the Henry Ford Health System (HFHS) Hospital in Detroit. All non-Covid CXRs were from pre-Covid era (2018-2019), and included images from both normal lungs and lungs affected by non-Covid pathologies. Training and test sets consisted of 2265 CXRs (1417 Covid negative, 848 Covid positive), and 1532 CXRs (945 Covid negative, 587 Covid positive), respectively. Six distinct cross-validation models, each trained on 1887 images and validated against 378 images, were combined into an ensemble model that was used to classify the CXR images of the test set with resulting Accuracy = 0.789, Precision = 0.739, Recall = 0.693, F1 score = 0.715, ROC(AUC) = 0.852.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 49 references that are in the map. First posted on arXiv 26 Feb 2021.

Builds on

27 of 49 references.

References · 27

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  5. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  6. 2017Axiomatic Attribution for Deep Networkscited by 3,350
  7. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
  8. 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,154
  9. 2017Learning Important Features Through Propagating Activation Differencescited by 1,454
  10. 2017SmoothGrad: removing noise by adding noisecited by 1,097
  11. 2018Image Transformercited by 770
  12. 2017Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentationscited by 614
15 more
  1. 2017Road Extraction by Deep Residual U-Netcited by 333
  2. 2020COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Imagescited by 264
  3. 2019ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed datacited by 260
  4. 2020COVID-19 Image Data Collectioncited by 100
  5. 2020Deep Learning COVID-19 Features on CXR using Limited Training Data Setscited by 68
  6. 2018Kymatio: Scattering Transforms in Pythoncited by 54
  7. 2014Deep Roto-Translation Scattering for Object Classificationcited by 49
  8. 2017Scaling the Scattering Transform: Deep Hybrid Networkscited by 35
  9. 2018Scattering Networks for Hybrid Representation Learningcited by 33
  10. 2019XLSor: A Robust and Accurate Lung Segmentor on Chest X-Rays Using Criss-Cross Attention and Customized Radiorealistic Abnormalities Generationcited by 28
  11. 2018Chest X-Ray Analysis of Tuberculosis by Deep Learning with Segmentation and Augmentationcited by 24
  12. 2020Lung Segmentation from Chest X-rays using Variational Data Imputationcited by 15
  13. 2017Fully Convolutional Architectures for Multi-Class Segmentation in Chest Radiographscited by 13
  14. 2016Inverse Problems with Invariant Multiscale Statisticscited by 3
  15. 2020Res-CR-Net, a residual network with a novel architecture optimized for the semantic segmentation of microscopy imagescited by 3

Built on by

Not cited yet.