pith. sign in

arxiv: 2006.05332 · v6 · submitted 2020-06-07 · 📡 eess.IV · cs.CV

Advance Warning Methodologies for COVID-19 using Chest X-Ray Images

Mete Ahishali , Aysen Degerli , Mehmet Yamac , Serkan Kiranyaz , Muhammad E. H. Chowdhury , Khalid Hameed , Tahir Hamid , Rashid Mazhar
show 1 more author
Moncef Gabbouj
This is my paper

Pith reviewed 2026-05-24 14:24 UTC · model grok-4.3

classification 📡 eess.IV cs.CV
keywords COVID-19 detectionChest X-rayEarly diagnosisMachine learningConvolutional Support Estimator NetworkEarly-QaTa-COV19 dataset
0
0 comments X

The pith

A compact classifier detects early-stage COVID-19 from chest X-rays with over 97 percent sensitivity and 95.5 percent specificity.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper evaluates several machine learning methods for spotting COVID-19 when infection traces are minimal or absent on chest X-ray images. It introduces the Early-QaTa-COV19 dataset containing 1065 early-stage cases labeled by doctors and more than 12,000 normal controls. Among the tested approaches, the Convolutional Support Estimator Network reaches the highest performance. This result matters because visual detection by experts is unreliable at this stage, so an automated system could support earlier intervention.

Core claim

The CSEN approach achieves the top (over 97%) sensitivity with over 95.5% specificity on the Early-QaTa-COV19 dataset for early-stage COVID-19 detection from chest X-ray images.

What carries the argument

Convolutional Support Estimator Network (CSEN), a compact classifier designed for scarce-data tasks that estimates class support to classify images.

If this is right

  • CSEN can serve as an advance warning tool before the disease reaches moderate or severe stages.
  • The Early-QaTa-COV19 dataset provides a public benchmark for testing other early-detection algorithms.
  • DenseNet-121 achieves 95 percent sensitivity and 99.74 percent specificity among the deep networks tested.
  • Compact classifiers like CSEN are competitive with or superior to larger deep networks on this scarce-data task.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Hospitals could add such a classifier to routine X-ray workflows for patients with respiratory symptoms.
  • The same support-estimation idea may apply to other conditions that show subtle early radiographic changes.
  • Performance on this dataset would need confirmation on images from multiple hospitals and imaging devices.

Load-bearing premise

The 1065 samples are accurately labeled as early-stage COVID-19 pneumonia with very limited or no visible infection signs by the medical doctors who created the dataset.

What would settle it

A blinded re-evaluation by independent radiologists of the 1065 images in Early-QaTa-COV19 that finds a substantial fraction do not meet the minimal-or-no-signs criterion.

Figures

Figures reproduced from arXiv: 2006.05332 by Aysen Degerli, Khalid Hameed, Mehmet Yamac, Mete Ahishali, Moncef Gabbouj, Muhammad E. H. Chowdhury, Rashid Mazhar, Serkan Kiranyaz, Tahir Hamid.

Figure 1
Figure 1. Figure 1: FIGURE 1 [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FIGURE 2 [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIGURE 3 [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: FIGURE 4 [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: FIGURE 5 [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: FIGURE 6 [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Accordingly, DenseNet-121 initiliazed with ChestX￾ray14 weights misses three more early case of COVID-19 than CSEN1, but it is able to provide much higher specificity (the sensitivity for normal X-ray images). It is observed that 10 VOLUME 9, 2021 [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: FIGURE 8 [PITH_FULL_IMAGE:figures/full_fig_p011_8.png] view at source ↗
read the original abstract

Coronavirus disease 2019 (COVID-19) has rapidly become a global health concern after its first known detection in December 2019. As a result, accurate and reliable advance warning system for the early diagnosis of COVID-19 has now become a priority. The detection of COVID-19 in early stages is not a straightforward task from chest X-ray images according to expert medical doctors because the traces of the infection are visible only when the disease has progressed to a moderate or severe stage. In this study, our first aim is to evaluate the ability of recent \textit{state-of-the-art} Machine Learning techniques for the early detection of COVID-19 from chest X-ray images. Both compact classifiers and deep learning approaches are considered in this study. Furthermore, we propose a recent compact classifier, Convolutional Support Estimator Network (CSEN) approach for this purpose since it is well-suited for a scarce-data classification task. Finally, this study introduces a new benchmark dataset called Early-QaTa-COV19, which consists of 1065 early-stage COVID-19 pneumonia samples (very limited or no infection signs) labelled by the medical doctors and 12 544 samples for control (normal) class. A detailed set of experiments shows that the CSEN achieves the top (over 97%) sensitivity with over 95.5% specificity. Moreover, DenseNet-121 network produces the leading performance among other deep networks with 95% sensitivity and 99.74% specificity.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper evaluates state-of-the-art machine learning classifiers, including a proposed Convolutional Support Estimator Network (CSEN), for early-stage COVID-19 detection from chest X-ray images. It introduces the Early-QaTa-COV19 benchmark dataset (1065 early-stage COVID-19 samples with very limited or no visible signs, labeled by medical doctors, plus 12,544 normal controls) and reports that CSEN achieves the highest performance with over 97% sensitivity and over 95.5% specificity, while DenseNet-121 leads among deep networks.

Significance. If the early-stage labels prove accurate and the experimental protocol is reproducible, the work would provide a valuable new benchmark dataset focused on the hardest early-detection regime and demonstrate that compact classifiers can be effective for scarce-data medical imaging tasks. The emphasis on advance warning rather than late-stage detection addresses a genuine clinical gap.

major comments (2)
  1. [Abstract / Dataset description] Abstract and dataset section: The headline claim that the 1065 samples are verifiably early-stage COVID-19 pneumonia with 'very limited or no visible infection signs' rests on labels supplied by the paper's medical collaborators, yet no inter-rater reliability statistics, explicit labeling protocol, adjudication process, or external validation are described. This is load-bearing for the central performance numbers.
  2. [Experimental results] Experimental evaluation (results section): The reported sensitivity and specificity figures for CSEN and other models are presented without any information on train-test splits, cross-validation strategy, class-imbalance handling (1065 vs. 12 544), or statistical significance testing. These omissions prevent independent verification of the claimed superiority.
minor comments (1)
  1. [Abstract] The abstract states performance numbers to one decimal place but does not indicate whether they are from a single run or averaged; adding this clarification would improve reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback highlighting key areas for improving clarity and reproducibility. We address each major comment below and will revise the manuscript to incorporate additional details where possible.

read point-by-point responses

  1. Referee: [Abstract / Dataset description] Abstract and dataset section: The headline claim that the 1065 samples are verifiably early-stage COVID-19 pneumonia with 'very limited or no visible infection signs' rests on labels supplied by the paper's medical collaborators, yet no inter-rater reliability statistics, explicit labeling protocol, adjudication process, or external validation are described. This is load-bearing for the central performance numbers.

    Authors: We acknowledge the importance of transparency in the labeling process. The Early-QaTa-COV19 samples were annotated by expert radiologists from our collaborating medical institutions based on clinical criteria for early-stage cases. The original submission does not include inter-rater reliability metrics or a full protocol description. In revision, we will add a dedicated subsection outlining the labeling criteria, expert review process, and any available validation steps used by the collaborators. The dataset will be released to support external scrutiny. revision: yes

  2. Referee: [Experimental results] Experimental evaluation (results section): The reported sensitivity and specificity figures for CSEN and other models are presented without any information on train-test splits, cross-validation strategy, class-imbalance handling (1065 vs. 12 544), or statistical significance testing. These omissions prevent independent verification of the claimed superiority.

    Authors: We agree that explicit experimental details are necessary for verification. The manuscript will be updated with a new experimental setup subsection specifying the train-test split strategy (stratified to address imbalance), cross-validation approach, imbalance handling techniques, and statistical significance tests applied to the performance comparisons. These revisions will enable independent reproduction of the results. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical performance on new dataset

full rationale

The paper reports experimental classification results (sensitivity/specificity) of CSEN and other networks on the newly introduced Early-QaTa-COV19 dataset. No derivation chain, equations, or 'predictions' exist that reduce the reported metrics to fitted parameters or self-referential quantities by construction. The central claim is direct empirical evaluation on held-out test samples; label provenance and inter-rater details are external to any mathematical reduction. This matches the default non-circular case for benchmark-style ML papers.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No mathematical model or derivation is present; the work is an empirical ML benchmark study on medical images. No free parameters, axioms, or invented entities are introduced beyond standard supervised classification assumptions.

pith-pipeline@v0.9.0 · 5838 in / 1096 out tokens · 24154 ms · 2026-05-24T14:24:51.065788+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

62 extracted references · 62 canonical work pages · 5 internal anchors

  1. [1]

    Coronavirus disease 2019 (COVID-19): A perspective from China,

    Z. Y . Zu, M. D. Jiang, P. P. Xu, W. Chen, Q. Q. Ni, G. M. Lu, and L. J. Zhang, “Coronavirus disease 2019 (COVID-19): A perspective from China,” Radiology, vol. 296, no. 2, 2020, doi: 10.1148/radiol.2020200490

    work page doi:10.1148/radiol.2020200490 2019

  2. [2]

    Laboratory testing for coronavirus disease 2019 (COVID-19) in suspected human cases: Interim guidance,

    World Health Organization, “Laboratory testing for coronavirus disease 2019 (COVID-19) in suspected human cases: Interim guidance,” World Health Organization, Tech. Rep., 2020

    work page 2019

  3. [3]

    Laboratory diagnosis and monitoring the viral shedding of 2019-nCoV infections,

    Y . Yang, M. Yang, C. Shen, F. Wang, J. Yuan, J. Li, M. Zhang, Z. Wang, L. Xing, J. Wei et al. , “Laboratory diagnosis and monitoring the viral shedding of 2019-nCoV infections,” MedRxiv, 2020

    work page 2019

  4. [4]

    Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: A report of 1014 cases,

    T. Ai, Z. Yang, H. Hou, C. Zhan, C. Chen, W. Lv, Q. Tao, Z. Sun, and L. Xia, “Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: A report of 1014 cases,” Radiology, vol. 296, no. 2, 2020, doi: 10.1148/radiol.2020200642

    work page doi:10.1148/radiol.2020200642 2019

  5. [5]

    Chest CT for typical 2019-nCoV pneumonia: Relationship to negative RT-PCR testing,

    X. Xie, Z. Zhong, W. Zhao, C. Zheng, F. Wang, and J. Liu, “Chest CT for typical 2019-nCoV pneumonia: Relationship to negative RT-PCR testing,” Radiology, vol. 296, no.2, 2020, doi: 10.1148/radiol.2020200343

    work page doi:10.1148/radiol.2020200343 2019

  6. [6]

    Sensitivity of chest CT for COVID-19: Comparison to RT-PCR,

    Y . Fang, H. Zhang, J. Xie, M. Lin, L. Ying, P. Pang, and W. Ji, “Sensitivity of chest CT for COVID-19: Comparison to RT-PCR,”Radiology, vol. 296, no. 2, 2020, doi: 10.1148/radiol.2020200432

    work page doi:10.1148/radiol.2020200432 2020

  7. [7]

    Computed tomography—an increasing source of radiation exposure,

    D. J. Brenner and E. J. Hall, “Computed tomography—an increasing source of radiation exposure,” N. Engl. J. Med., vol. 357, no. 22, pp. 2277– 2284, 2007

    work page 2007

  8. [8]

    The role of chest imaging in patient management during the COVID-19 pandemic: A multinational consensus statement from the Fleischner Society,

    G. D. Rubin, C. J. Ryerson, L. B. Haramati, N. Sverzellati, J. P. Kanne, S. Raoof, N. W. Schluger, A. V olpi, J.-J. Yim, I. B. Martinet al., “The role of chest imaging in patient management during the COVID-19 pandemic: A multinational consensus statement from the Fleischner Society,” Chest, vol. 158, no. 1, pp. 106–116, 2020

    work page 2020

  9. [9]

    Can AI help in screening Viral and COVID-19 pneumonia?

    M. E. Chowdhury, T. Rahman, A. Khandakar, R. Mazhar, M. A. Kadir, Z. B. Mahbub, K. R. Islam, M. S. Khan, A. Iqbal, N. Al-Emadi et al. , “Can AI help in screening Viral and COVID-19 pneumonia?” arXiv:2003.13145, 2020. [Online]. Available: https://arxiv.org/abs/2003. 13145

    work page arXiv 2003

  10. [10]

    Weakly supervised deep learning for COVID-19 infection detection and classification from CT images,

    S. Hu, Y . Gao, Z. Niu, Y . Jiang, L. Li, X. Xiao, M. Wang, E. F. Fang, W. Menpes-Smith, J. Xia et al. , “Weakly supervised deep learning for COVID-19 infection detection and classification from CT images,” IEEE Access, vol. 8, pp. 118 869–118 883, 2020

    work page 2020

  11. [11]

    COVID-19: Automatic de- tection from X-ray images utilizing transfer learning with convolutional neural networks,

    I. D. Apostolopoulos and T. A. Mpesiana, “COVID-19: Automatic de- tection from X-ray images utilizing transfer learning with convolutional neural networks,” Phys. Eng. Sci. Med., vol. 43, pp. 635–640, 2020

    work page 2020

  12. [12]

    Finding COVID-19 from chest X-rays using deep learning on a small dataset,

    L. O. Hall, R. Paul, D. B. Goldgof, and G. M. Goldgof, “Finding COVID-19 from chest X-rays using deep learning on a small dataset,” arXiv:2004.02060, 2020. [Online]. Available: https://arxiv.org/abs/2004. 02060

    work page arXiv 2004

  13. [13]

    Information-theoretic bounds on sparsity recovery in the high-dimensional and noisy setting,

    M. Wainwright, “Information-theoretic bounds on sparsity recovery in the high-dimensional and noisy setting,” in Proc. IEEE Int. Symp. Inf. Theory (ISIT), 2007, pp. 961–965

    work page 2007

  14. [14]

    Convolutional Sparse Support Estimator Based Covid-19 Recognition from X-ray Images

    M. Yamac, M. Ahishali, A. Degerli, S. Kiranyaz, M. E. H. Chowdhury, and M. Gabbouj, “Convolutional sparse support estimator based COVID- 19 recognition from X-ray images,” arXiv:2005.04014, 2020. [Online]. Available: https://arxiv.org/abs/2005.04014

    work page internal anchor Pith review Pith/arXiv arXiv 2005

  15. [15]

    Machine learning for COVID-19 detection and prognostication using chest radiographs and CT scans: A systematic methodological review,

    M. Roberts, D. Driggs, M. Thorpe, J. Gilbey, M. Yeung, S. Ursprung, A. I. Aviles-Rivero, C. Etmann, C. McCague, L. Beeret al., “Machine learning for COVID-19 detection and prognostication using chest radiographs and CT scans: A systematic methodological review,”arXiv:2008.06388, 2020. [Online]. Available: https://arxiv.org/abs/2008.06388

    work page arXiv 2008

  16. [16]

    COVID-19 infection map generation and detection from chest X-ray images,

    A. Degerli, M. Ahishali, M. Yamac, S. Kiranyaz, M. E. Chowdhury, K. Hameed, T. Hamid, R. Mazhar, and M. Gabbouj, “COVID-19 infection map generation and detection from chest X-ray images,” arXiv:2009.12698, 2020. [Online]. Available: https://arxiv.org/abs/2009. 12698

    work page arXiv 2009

  17. [17]

    Frequency and distribution of chest radiographic findings in COVID- 19 positive patients,

    H. Y . F. Wong, H. Y . S. Lam, A. H.-T. Fong, S. T. Leung, T. W.-Y . Chin, C. S. Y . Lo, M. M.-S. Lui, J. C. Y . Lee, K. W.-H. Chiu, T. Chung et al., “Frequency and distribution of chest radiographic findings in COVID- 19 positive patients,” Radiology, vol. 296, no. 2, 2020, doi: 10.1148/ra- diol.2020201160

    work page doi:10.1148/ra- 2020

  18. [18]

    SaliencyGAN: Deep learning semisupervised salient object detection in the fog of IoT,

    C. Wang, S. Dong, X. Zhao, G. Papanastasiou, H. Zhang, and G. Yang, “SaliencyGAN: Deep learning semisupervised salient object detection in the fog of IoT,” IEEE Trans. Ind. Informat., vol. 16, no. 4, pp. 2667–2676, 2019

    work page 2019

  19. [19]

    Industrial cyber-physical systems-based cloud IoT edge for federated heterogeneous distillation,

    C. Wang, G. Yang, G. Papanastasiou, H. Zhang, J. Rodrigues, and V . Al- buquerque, “Industrial cyber-physical systems-based cloud IoT edge for federated heterogeneous distillation,” IEEE Trans. Ind. Informat. , 2020, doi: 10.1109/TII.2020.3007407

    work page doi:10.1109/tii.2020.3007407 2020

  20. [20]

    Machine learning for IoT systems,

    A. Khattab and N. Youssry, “Machine learning for IoT systems,” in Internet of Things (IoT). Springer, 2020, pp. 105–127

    work page 2020

  21. [21]

    Robust face recognition via sparse representation,

    J. Wright, A. Y . Yang, A. Ganesh, S. S. Sastry, and Y . Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 2, pp. 210–227, 2008

    work page 2008

  22. [22]

    Sparse representation for computer vision and pattern recognition,

    J. Wright, Y . Ma, J. Mairal, G. Sapiro, T. S. Huang, and S. Yan, “Sparse representation for computer vision and pattern recognition,” Proc. IEEE, vol. 98, no. 6, pp. 1031–1044, 2010

    work page 2010

  23. [23]

    Sparse representation or collaborative representation: Which helps face recognition?

    L. Zhang, M. Yang, and X. Feng, “Sparse representation or collaborative representation: Which helps face recognition?” in Proc. IEEE Int. Conf. Comput. Vision (ICCV), 2011, pp. 471–478

    work page 2011

  24. [25]

    Convolutional Sparse Support Estimator Network (CSEN) From energy efficient support estimation to learning-aided Compressive Sensing

    [Online]. Available: https://arxiv.org/abs/2003.00768

    work page internal anchor Pith review Pith/arXiv arXiv 2003

  25. [26]

    Densely connected convolutional networks,

    G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proc. IEEE Conf. Comput. Vision and Pattern Recognit. (CVPR), 2017, pp. 4700–4708

    work page 2017

  26. [27]

    Deep residual learning for image recognition,

    K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. Comput. Vision and Pattern Recognit. (CVPR), 2016, pp. 770–778

    work page 2016

  27. [28]

    Rethinking the inception architecture for computer vision,

    C. Szegedy, V . Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in Proc. IEEE Conf. Comput. Vision and Pattern Recognit. (CVPR), 2016, pp. 2818–2826

    work page 2016

  28. [29]

    Optimally sparse representation in general (nonorthogonal) dictionaries via l1 minimization,

    D. L. Donoho and M. Elad, “Optimally sparse representation in general (nonorthogonal) dictionaries via l1 minimization,” Proc. Nat. Acad. Sci. , vol. 100, no. 5, pp. 2197–2202, 2003

    work page 2003

  29. [30]

    Atomic decomposition by basis pursuit,

    S. S. Chen, D. L. Donoho, and M. A. Saunders, “Atomic decomposition by basis pursuit,” Soc. Ind. Appl. Math. Rev., vol. 43, no. 1, pp. 129–159, 2001

    work page 2001

  30. [31]

    Chestx- ray8: Hospital-scale chest X-ray database and benchmarks on weakly- supervised classification and localization of common thorax diseases,

    X. Wang, Y . Peng, L. Lu, Z. Lu, M. Bagheri, and R. M. Summers, “Chestx- ray8: Hospital-scale chest X-ray database and benchmarks on weakly- supervised classification and localization of common thorax diseases,” in Proc. IEEE Conf. Comput. Vision and Pattern Recognit. (CVPR), 2017, pp. 2097–2106

    work page 2017

  31. [32]

    CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning

    P. Rajpurkar, J. Irvin, K. Zhu, B. Yang, H. Mehta, T. Duan, D. Ding, A. Bagul, C. Langlotz, K. Shpanskaya et al. , “Chexnet: Radiologist- level pneumonia detection on chest X-rays with deep learning,” arXiv:1711.05225, 2017. [Online]. Available: https://arxiv.org/abs/1711. 05225

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  32. [33]

    A survey on representation-based classification and detection in hyperspectral remote sensing imagery,

    W. Li and Q. Du, “A survey on representation-based classification and detection in hyperspectral remote sensing imagery,” Pattern Recognit. Lett., vol. 83, pp. 115–123, 2016

    work page 2016

  33. [34]

    Learning sparse representations for human action recognition,

    T. Guha and R. K. Ward, “Learning sparse representations for human action recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 8, pp. 1576–1588, 2011

    work page 2011

  34. [35]

    A probabilistic and ripless theory of compressed sensing,

    E. J. Candes and Y . Plan, “A probabilistic and ripless theory of compressed sensing,” IEEE Trans. Inf. Theory, vol. 57, no. 11, pp. 7235–7254, 2011

    work page 2011

  35. [36]

    Limits on support recovery with probabilistic models: An information-theoretic framework,

    J. Scarlett and V . Cevher, “Limits on support recovery with probabilistic models: An information-theoretic framework,” IEEE Trans. Inf. Theory , vol. 63, no. 1, pp. 593–620, 2016

    work page 2016

  36. [37]

    Sampling bounds for sparse support recovery in the presence of noise,

    G. Reeves and M. Gastpar, “Sampling bounds for sparse support recovery in the presence of noise,” inProc. IEEE Int. Symp. Inf. Theory (ISIT), 2008, pp. 2187–2191

    work page 2008

  37. [38]

    Approximate sparsity pattern recovery: Information-theoretic lower bounds,

    G. Reeves and M. C. Gastpar, “Approximate sparsity pattern recovery: Information-theoretic lower bounds,” IEEE Trans. Inf. Theory , vol. 59, no. 6, pp. 3451–3465, 2013. 12 VOLUME 9, 2021 Ahishali et al.:Advance Warning Methodologies for COVID-19 using Chest X-Ray Images

    work page 2013

  38. [39]

    Information-theoretic limits on sparse support recovery: Dense versus sparse measurements,

    W. Wang, M. J. Wainwright, and K. Ramchandran, “Information-theoretic limits on sparse support recovery: Dense versus sparse measurements,” in Proc. IEEE Int. Symp. Inf. Theory (ISIT), 2008, pp. 2197–2201

    work page 2008

  39. [40]

    Nearly sharp sufficient conditions on exact sparsity pattern recovery,

    K. R. Rad, “Nearly sharp sufficient conditions on exact sparsity pattern recovery,” IEEE Trans. Inf. Theory, vol. 57, no. 7, pp. 4672–4679, 2011

    work page 2011

  40. [41]

    Reconnet: Non-iterative reconstruction of images from compressively sensed mea- surements,

    K. Kulkarni, S. Lohit, P. Turaga, R. Kerviche, and A. Ashok, “Reconnet: Non-iterative reconstruction of images from compressively sensed mea- surements,” in Proc. IEEE Conf. Comput. Vision and Pattern Recognit. (CVPR), 2016, pp. 449–458

    work page 2016

  41. [42]

    Amp-inspired deep networks for sparse linear inverse problems,

    M. Borgerding, P. Schniter, and S. Rangan, “Amp-inspired deep networks for sparse linear inverse problems,” IEEE Trans. Signal Process., vol. 65, no. 16, pp. 4293–4308, 2017

    work page 2017

  42. [43]

    Compres- sively sensed image recognition,

    A. De ˘gerli, S. Aslan, M. Yamac, B. Sankur, and M. Gabbouj, “Compres- sively sensed image recognition,” in Proc. Eur. Workshop Vis. Inf. Process. (EUVIP), 2018, doi: 10.1109/EUVIP.2018.8611657

    work page doi:10.1109/euvip.2018.8611657 2018

  43. [44]

    Direct inference on compressive measurements using convolutional neural networks,

    S. Lohit, K. Kulkarni, and P. Turaga, “Direct inference on compressive measurements using convolutional neural networks,” in Proc. IEEE Int. Conf. Image Process. (ICIP), 2016, pp. 1913–1917

    work page 2016

  44. [45]

    Multilin- ear compressive learning,

    D. T. Tran, M. Yamaç, A. Degerli, M. Gabbouj, and A. Iosifidis, “Multilin- ear compressive learning,” IEEE Trans. Neural Netw. Learn. Syst. , 2020, doi: 10.1109/TNNLS.2020.2984831

    work page doi:10.1109/tnnls.2020.2984831 2020

  45. [46]

    BIMCV COVID-19+: A large annotated dataset of RX and CT images from COVID-19 patients,

    M. d. l. I. Vayá, J. M. Saborit, J. A. Montell, A. Pertusa, A. Bustos, M. Cazorla, J. Galant, X. Barber, D. Orozco-Beltrán, F. Garcia et al. , “BIMCV COVID-19+: A large annotated dataset of RX and CT images from COVID-19 patients,” arXiv:2006.01174, 2020. [Online]. Available: https://arxiv.org/abs/2006.01174

    work page arXiv 2006

  46. [47]

    COVID-19 image repository,

    “COVID-19 image repository,” https://github.com/ml-workgroup/ covid-19-image-repository, 2020, (accessed 16 September 2020)

    work page 2020

  47. [48]

    COVID-19 database,

    “COVID-19 database,” https://www.sirm.org/category/senza-categoria/ covid-19/, 2020, (accessed 16 September 2020)

    work page 2020

  48. [49]

    COVID-19 Spain CXR,

    “COVID-19 Spain CXR,” https://threadreaderapp.com/thread/ 1243928581983670272.html, 2020, (accessed 16 September 2020)

    work page 2020

  49. [50]

    Radiopaedia COVID-19 CXR,

    “Radiopaedia COVID-19 CXR,” https://radiopaedia.org/playlists/25975? lang=us, 2020, (accessed 16 September 2020)

    work page 2020

  50. [51]

    Padchest: A large chest X-ray image dataset with multi-label annotated reports,

    A. Bustos, A. Pertusa, J.-M. Salinas, and M. de la Iglesia-Vayá, “Padchest: A large chest X-ray image dataset with multi-label annotated reports,” Med. Image Anal., 2020, doi: 10.1016/j.media.2020.101797

    work page doi:10.1016/j.media.2020.101797 2020

  51. [52]

    RSNA pneumonia detection challenge,

    “RSNA pneumonia detection challenge,” https://www.kaggle.com/c/ rsna-pneumonia-detection-challenge/overview, 2018, (accessed 22 September 2020)

    work page 2018

  52. [53]

    Preparing a collection of radiology examinations for distribution and retrieval,

    D. Demner-Fushman, M. D. Kohli, M. B. Rosenman, S. E. Shooshan, L. Rodriguez, S. Antani, G. R. Thoma, and C. J. McDonald, “Preparing a collection of radiology examinations for distribution and retrieval,” J. Amer. Med. Inform. Assoc., vol. 23, no. 2, pp. 304–310, 2016

    work page 2016

  53. [54]

    Two public chest X-ray datasets for computer-aided screening of pul- monary diseases,

    S. Jaeger, S. Candemir, S. Antani, Y .-X. J. Wáng, P.-X. Lu, and G. Thoma, “Two public chest X-ray datasets for computer-aided screening of pul- monary diseases,” Quant. Imaging Med. Surg., vol. 4, no. 6, pp. 475–477, 2014

    work page 2014

  54. [55]

    Chest X-ray images (pneumonia),

    “Chest X-ray images (pneumonia),” https://www.kaggle.com/ paultimothymooney/chest-xray-pneumonia, 2018, (accessed 16 September 2020)

    work page 2018

  55. [56]

    TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

    M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin et al. , “Tensorflow: Large-scale machine learning on heterogeneous distributed systems,” arXiv:1603.04467, 2016. [Online]. Available: https://arxiv.org/abs/1603. 04467

    work page internal anchor Pith review Pith/arXiv arXiv 2016

  56. [57]

    Adam: A Method for Stochastic Optimization

    D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv:1412.6980, 2014. [Online]. Available: https://arxiv.org/abs/1412. 6980

    work page internal anchor Pith review Pith/arXiv arXiv 2014

  57. [58]

    Fast l1-minimization algorithms for robust face recognition,

    A. Y . Yang, Z. Zhou, A. G. Balasubramanian, S. S. Sastry, and Y . Ma, “Fast l1-minimization algorithms for robust face recognition,” IEEE Trans. Image Process., vol. 22, no. 8, pp. 3234–3246, 2013

    work page 2013

  58. [59]

    An interior-point method for large-scale l1-regularized logistic regression,

    K. Koh, S.-J. Kim, and S. Boyd, “An interior-point method for large-scale l1-regularized logistic regression,” J. Mach. Learn. Res., vol. 8, pp. 1519– 1555, 2007

    work page 2007

  59. [60]

    Distributed optimization and statistical learning via the alternating direction method of multipliers,

    S. Boyd, N. Parikh, E. Chu, B. Peleato, J. Eckstein et al. , “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Found. Trends Mach. Learn. , vol. 3, no. 1, 2011, doi: 10.1561/2200000016

    work page doi:10.1561/2200000016 2011

  60. [61]

    Homotopy continuation for sparse signal representation,

    D. M. Malioutov, M. Cetin, and A. S. Willsky, “Homotopy continuation for sparse signal representation,” in Proc. IEEE Int. Conf. Acoust., Speech, and Signal Process. (ICASSP), vol. 5, 2005, pp. 733–736

    work page 2005

  61. [62]

    Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems,

    M. A. Figueiredo, R. D. Nowak, and S. J. Wright, “Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems,” IEEE J. Sel. Topics Signal Process. , vol. 1, no. 4, pp. 586–597, 2007

    work page 2007

  62. [63]

    l1-magic: Recovery of sparse signals via con- vex programming,

    E. Candes and J. Romberg, “ l1-magic: Recovery of sparse signals via con- vex programming,” Caltech, Tech. Rep., 2005. [Online]. Available: https: //statweb.stanford.edu/~candes/software/l1magic/downloads/l1magic.pdf METE AHISHALI received the B.Sc. degree (Hons.) in Electrical and Electronics Engineer- ing from Izmir University of Economics, Izmir, Turke...

    work page 2005