Fetching the paper…
Reading the bibliography…
In this work we design an end-to-end deep learning architecture for predicting, on Chest X-rays images (CXR), a multi-regional score conveying the degree of lung compromise in COVID-19 patients.
EfficientDet: Scalable and Efficient Object Detection
Tan, M., Pang, R., Le, Q.V., 2019 · 1911
Earlier work this paper cites.
Deep learning with noisy labels: exploring techniques and remedies in medical image analysis
Karimi, D., Dou, H., Warfield, S.K., Gholipour, A., 2019 · 1912
Earlier work this paper cites.
Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules
Shiraishi, J., Katsuragawa, S., Ikezoe, J., Matsumoto, T., Kobayashi, T., Komatsu, K.i., Matsui, M., Fujita, H., Kodera, Y., Doi, K., 2000 · 2000
Earlier work this paper cites.
COVID-19 image data collection
Cohen, J.P., Morrison, P., Dao, L., 2020b · 2003
Earlier work this paper cites.
Gozes, O., Frid-Adar, M., Greenspan, H., Browning, P.D., Zhang, H., Ji, W., Bernheim, A., Siegel, E., 2020 · 2003
Earlier work this paper cites.
Linda Wang, Z.Q.L., Wong, A., 2020 · 2003
Earlier work this paper cites.
COVID-19: A survey on public medical imaging data resources
Kalkreuth, R., Kaufmann, P., 2020 · 2004
Earlier work this paper cites.
DeepCOVIDExplainer: Explainable COVID-19 predictions based on chest x-ray images
Karim, M.R., Döhmen, T., Rebholz-Schuhmann, D., Decker, S., Cochez, M., Beyan, O., 2020 · 2004
Earlier work this paper cites.
COVID-MobileXpert: On-device COVID-19 screening using snapshots of chest x-ray
Li, X., Li, C., Zhu, D., 2020c · 2004
Earlier work this paper cites.
A Critic Evaluation of Methods for COVID-19 Automatic Detection from X-Ray Images
Maguolo, G., Nanni, L., 2020 · 2004
Earlier work this paper cites.
Iteratively pruned deep learning ensembles for COVID-19 detection in chest X-rays
Rajaraman, S., Siegelman, J., Alderson, P.O., Folio, L.S., Folio, L.R., Antani, S.K., 2020 · 2004
Earlier work this paper cites.
Unveiling COVID-19 from chest x-ray with deep learning: a hurdles race with small data
Tartaglione, E., Barbano, C.A., Berzovini, C., Calandri, M., Grangetto, M., 2020 · 2004
Earlier work this paper cites.
Predicting COVID-19 pneumonia severity on chest x-ray with deep learning
Cohen, J.P., Dao, L., Morrison, P., Roth, K., Bengio, Y., Shen, B., Abbasi, A., Hoshmand-Kochi, M., Ghassemi, M., Li, H., Duong, T.Q., 2020a · 2005
Earlier work this paper cites.
Wong, A., Lin, Z.Q., Wang, L., Chung, A.G., Shen, B., Abbasi, A., Hoshmand-Kochi, M., Duong, T.Q., 2020a · 2005
Earlier work this paper cites.
Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database
van Ginneken, B., Stegmann, M.B., Loog, M., 2006 · 2006
Earlier work this paper cites.
Covid-19 in cxr: from detection and severity scoring to patient disease monitoring
Amer, R., Frid-Adar, M., Gozes, O., Nassar, J., Greenspan, H., 2020 · 2008
Earlier work this paper cites.
Quick shift and kernel methods for mode seeking, in: Forsyth, D., Torr, P., Zisserman, A. (Eds.), European Conference of Computer Vision (ECCV), Springer Berlin Heidelberg, Berlin, Heidelberg. pp. 705–718
Vedaldi, A., Soatto, S., 2008 · 2008
Earlier work this paper cites.
Hryniewska, W., Bombiński, P., Szatkowski, P., Tomaszewska, P., Przelaskowski, A., Biecek, P., 2020 · 2012
Earlier work this paper cites.
Lung Segmentation in Chest Radiographs Using Anatomical Atlases With Nonrigid Registration
Candemir, S., Jaeger, S., Palaniappan, K., Musco, J.P., Singh, R.K., Xue, Z., Karargyris, A., Antani, S., Thoma, G., McDonald, C.J., 2014 · 2014
Earlier work this paper cites.
Two public chest x-ray datasets for computer-aided screening of pulmonary diseases
Jaeger, S., Candemir, S., Antani, S., Wáng, Y.X.J., Lu, P.X., Thoma, G., 2014 · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D.P., Ba, J., 2014 · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A., 2014 · 2014
Earlier work this paper cites.
Weakly supervised histopathology cancer image segmentation and classification
Xu, Y., Zhu, J.Y., Chang, E.I.C., Lai, M., Tu, Z., 2014 · 2014
Earlier work this paper cites.
Fully convolutional networks for semantic segmentation, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3431–3440
Long, J., Shelhamer, E., Darrell, T., 2015 · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation, in: International Conference on Medical image computing and computer-assisted intervention, Springer. pp. 234–241
Ronneberger, O., Fischer, P., Brox, T., 2015 · 2015
Cited alongside, same era.
Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778
He, K., Zhang, X., Ren, S., Sun, J., 2016 · 2016
Cited alongside, same era.
“Why Should I Trust You?”: Explaining the Predictions of Any Classifier, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, New York, NY, USA. p. 1135–1144
Ribeiro, M.T., Singh, S., Guestrin, C., 2016 · 2016
Cited alongside, same era.
Glasmachers, T., 2017 · 2017
Cited alongside, same era.
Artificial intelligence applied on chest X-ray can aid in the diagnosis of COVID-19 infection: a first experience from lombardy, italy
Castiglioni, I., Ippolito, D., Interlenghi, M., Monti, C.B., Salvatore, C., Schiaffino, S., Polidori, A., Gandola, D., Messa, C., Sardanelli, F., 2020 · 2020
Closest in time.
COVID-19 image data collection: Prospective predictions are the future
Cohen, J.P., Morrison, P., Dao, L., Roth, K., Duong, T.Q., Ghassemi, M., 2020c · 2020
Closest in time.
Serial quantitative chest ct assessment of COVID-19: Deep-learning approach
Huang, L., Han, R., Ai, T., Yu, P., Kang, H., Tao, Q., Xia, L., 2020 · 2020
Closest in time.
How might ai and chest imaging help unravel COVID-19’mysteries?
Kundu, S., Elhalawani, H., Gichoya, J.W., Kahn, C.E., 2020 · 2020
Closest in time.
Cautions about radiologic diagnosis of COVID-19 infection driven by artificial intelligence
Laghi, A., 2020 · 2020
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Densely connected convolutional networks, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700–4708
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q., 2017 · 2017
Cited alongside, same era.
Feature Pyramid Networks for Object Detection, in: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Lin, T.Y., Dollar, P., Girshick, R., He, K., Hariharan, B., Belongie, S., 2017 · 2017
Cited alongside, same era.
Searching for activation functions
Ramachandran, P., Zoph, B., Le, Q.V., 2017 · 2017
Cited alongside, same era.
Grad-CAM: Visual Explanations From Deep Networks via Gradient-Based Localization, in: The IEEE International Conference on Computer Vision (ICCV)
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D., 2017 · 2017
Cited alongside, same era.
Inception-v4, inception-resnet and the impact of residual connections on learning, in: Thirty-first AAAI conference on artificial intelligence
Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A., 2017 · 2017
Cited alongside, same era.
Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3462–3471
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M., 2017 · 2017
Cited alongside, same era.
A brief introduction to weakly supervised learning
Zhou, Z.H., 2017 · 2017
Cited alongside, same era.
Improving the segmentation of anatomical structures in chest radiographs using u-net with an imagenet pre-trained encoder, in: Image Analysis for Moving Organ, Breast, and Thoracic Images. Springer, pp. 159–168
Frid-Adar, M., Ben-Cohen, A., Amer, R., Greenspan, H., 2018 · 2018
Cited alongside, same era.
Leveraging Data Science To Combat COVID-19: A Comprehensive Review
Latif, S., Usman, M., Manzoor, S., Iqbal, W., Qadir, J., Tyson, G., Castro, I., Razi, A., Boulos, M.N.K., Weller, A., Crowcroft, J., 2020 · 2020
Closest in time.
Automated Assessment of CO-RADS and Chest CT Severity Scores in Patients with Suspected COVID-19 Using Artificial Intelligence
Lessmann, N., Sanchez, C.I., Beenen, L., Boulogne, L.H., Brink, M., Calli, E., Charbonnier, J.P., Dofferhoff, T., van Everdingen, W.M., Gerke, P.K., Geurts, B., Gietema, H.A., Groeneveld, M., van Harten, L., Hendrix, N., Hendrix, W., Huisman, H.J., IÅ¡gum, I., Jacobs, C., Kluge, R., Kok, M., Krdzalic, J., Lassen-Schmidt, B., van Leeuwen, K., Meakin, J., Overkamp, M., van Rees Vellinga, T., van Rikxoort, E.M., Samperna, R., Schaefer-Prokop, C., Schalekamp, S., Scholten, E.T., Sital, C., Stager, L., Teuwen, J., Vaidhya Venkadesh, K., de Vente, C., Vermaat, M., Xie, W., de Wilde, B., Prokop, M., van Ginneken, B., 2020 · 2020
Closest in time.
Improvement and multi-population generalizability of a deep learning-based chest radiograph severity score for covid-19
Li, M.D., Arun, N.T., Aggarwal, M., Gupta, S., Singh, P., Little, B.P., Mendoza, D.P., Corradi, G.C., Takahashi, M.S., Ferraciolli, S.F., Succi, M.D., Lang, M., Bizzo, B.C., Dayan, I., Kitamura, F.C., Kalpathy-Cramer, J., 2020a · 2020
Closest in time.
COVID-19: A Multimodality Review of Radiologic Techniques, Clinical Utility, and Imaging Features
Manna, S., Wruble, J., Maron, S., Toussie, D., Voutsinas, N., Finkelstein, M., Cedillo, M.A., Diamond, J., Eber, C., Jacobi, A., Chung, M., Bernheim, A., 2020 · 2020
Closest in time.
Which role for chest x-ray score in predicting the outcome in covid-19 pneumonia?
Maroldi, R., Rondi, P., Agazzi, G.M., Ravanelli, M., Borghesi, A., Farina, D., 2020 · 2020
Closest in time.
Deep learning COVID-19 features on cxr using limited training data sets
Oh, Y., Park, S., Ye, J.C., 2020 · 2020
Closest in time.
COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios
Pereira, R.M., Bertolini, D., Teixeira, L.O., Silla, C.N., Costa, Y.M., 2020 · 2020
Closest in time.
On the Interpretability of Artificial Intelligence in Radiology: Challenges and Opportunities
Reyes, M., Meier, R., Pereira, S., Silva, C.A., Dahlweid, F.M., Tengg-Kobligk, H.v., Summers, R.M., Wiest, R., 2020 · 2020
Closest in time.
The Role of Chest Imaging in Patient Management during the COVID-19 Pandemic: A Multinational Consensus Statement from the Fleischner Society
Rubin, G.D., Ryerson, C.J., Haramati, L.B., Sverzellati, N., Kanne, J.P., Raoof, S., Schluger, N.W., Volpi, A., Yim, J.J., Martin, I.B.K., Anderson, D.J., Kong, C., Altes, T., Bush, A., Desai, S.R., Goldin, J., Goo, J.M., Humbert, M., Inoue, Y., Kauczor, H.U., Luo, F., Mazzone, P.J., Prokop, M., Remy-Jardin, M., Richeldi, L., Schaefer-Prokop, C.M., Tomiyama, N., Wells, A.U., Leung, A.N., 2020 · 2020
Closest in time.
Assessing the Value of Diagnostic Tests in the New World of COVID-19 Pandemic
Sardanelli, F., Di Leo, G., 2020 · 2020
Closest in time.
Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19
Shi, F., Wang, J., Shi, J., Wu, Z., Wang, Q., Tang, Z., He, K., Shi, Y., Shen, D., 2020 · 2020
Closest in time.
Artificial intelligence of covid-19 imaging: A hammer in search of a nail
Summers, R.M., 2020 · 2020
Closest in time.
Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation
Tajbakhsh, N., Jeyaseelan, L., Li, Q., Chiang, J.N., Wu, Z., Ding, X., 2020 · 2020
Closest in time.
Digital technology and COVID-19
Ting, D.S.W., Carin, L., Dzau, V., Wong, T.Y., 2020 · 2020
Closest in time.
Clinical and chest radiography features determine patient outcomes in young and middle age adults with COVID-19
Toussie, D., Voutsinas, N., Finkelstein, M., Cedillo, M.A., Manna, S., Maron, S.Z., Jacobi, A., Chung, M., Bernheim, A., Eber, C., Concepcion, J., Fayad, Z., Gupta, Y.S., 2020 · 2020
Closest in time.
Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs
Zhu, J., Shen, B., Abbasi, A., Hoshmand-Kochi, M., Li, H., Duong, T.Q., 2020 · 2020
Closest in time.
Determination of disease severity in COVID-19 patients using deep learning in chest X-ray images
Blain, M., Kassin, M.T., Varble, N., Wang, X., Xu, Z., Xu, D., Carrafiello, G., Vespro, V., Stellato, E., Ierardi, A.M., et al., 2021 · 2021
Closest in time.
Spatial Transformer Networks, in: Advances in Neural Information Processing Systems 28. Curran Associates, Inc., pp. 2017–2025
Jaderberg, M., Simonyan, K., Zisserman, A., kavukcuoglu, k., 2015 · 2025
Closest in time.