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Developing a robust algorithm to diagnose and quantify the severity of COVID-19 using Chest X-ray (CXR) requires a large number of well-curated COVID-19 datasets, which is difficult to collect under the global COVID-19 pandemic.
Full-gradient representation for neural network visualization
Srinivas, S., Fleuret, F., 2019 · 1905
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Covidx-net: A framework of deep learning classifiers to diagnose COVID-19 in x-ray images
Hemdan, E.E.D., Shouman, M.A., Karar, M.E., 2020 · 2003
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Narin, A., Kaya, C., Pamuk, Z., 2020 · 2003
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COVID-19 screening on chest x-ray images using deep learning based anomaly detection
Zhang, J., Xie, Y., Li, Y., Shen, C., Xia, Y., 2020 · 2003
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COVIDNet-S: Towards computer-aided severity assessment via training and validation of deep neural networks for geographic extent and opacity extent scoring of chest X-rays for SARS-CoV-2 lung disease severity
Wong, A., Qiu Lin, Z., Wang, L., Chung, A.G., Shen, B., Abbasi, A., Hoshmand-Kochi, M., Duong, T.Q., 2020 · 2005
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Weakly supervised lesion localization with probabilistic-cam pooling
Ye, W., Yao, J., Xue, H., Li, Y., 2020 · 2005
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COVID-19 image data collection: Prospective predictions are the future
Cohen, J.P., Morrison, P., Dao, L., Roth, K., Duong, T.Q., Ghassemi, M., 2020b · 2006
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Bimcv COVID-19+: a large annotated dataset of rx and ct images from COVID-19 patients
De La Iglesia Vayá, M., Saborit, J.M., Montell, J.A., Pertusa, A., Bustos, A., Cazorla, M., Galant, J., Barber, X., Orozco-Beltrán, D., García-García, F., et al., 2020 · 2006
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End-to-end learning for semiquantitative rating of COVID-19 severity on chest X-rays
Signoroni, A., Savardi, M., Benini, S., Adami, N., Leonardi, R., Gibellini, P., Vaccher, F., Ravanelli, M., Borghesi, A., Maroldi, R., Farina, D., 2020a · 2006
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End-to-end learning for semiquantitative rating of COVID-19 severity on chest x-rays
Signoroni, A., Savardi, M., Benini, S., Adami, N., Leonardi, R., Gibellini, P., Vaccher, F., Ravanelli, M., Borghesi, A., Maroldi, R., et al., 2020b · 2006
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al., 2020 · 2010
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Deformable DETR: Deformable transformers for end-to-end object detection
Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J., 2020b · 2010
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Transformer interpretability beyond attention visualization
Chefer, H., Gur, S., Wolf, L., 2020 · 2012
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Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers
Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., Torr, P.H., et al., 2020b · 2012
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.R., Samek, W., 2015 · 2015
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Grad-CAM: Visual explanations from deep networks via gradient-based localization, in: Proceedings of the IEEE international conference on computer vision, pp. 618–626
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D., 2017 · 2017
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SmoothGrad: removing noise by adding noise
Smilkov, D., Thorat, N., Kim, B., Viégas, F., Wattenberg, M., 2017 · 2017
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COVID-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks
Apostolopoulos, I.D., Mpesiana, T.A., 2020 · 2020
Later among the works it cites.
Chest CT findings in coronavirus disease-19 (COVID-19): relationship to duration of infection
Bernheim, A., Mei, X., Huang, M., Yang, Y., Fayad, Z.A., Zhang, N., Diao, K., Lin, B., Zhu, X., Li, K., et al., 2020 · 2020
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COVID-19 outbreak in italy: experimental chest x-ray scoring system for quantifying and monitoring disease progression
Borghesi, A., Maroldi, R., 2020 · 2020
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The challenges of deploying artificial intelligence models in a rapidly evolving pandemic
Hu, Y., Jacob, J., Parker, G.J., Hawkes, D.J., Hurst, J.R., Stoyanov, D., 2020 · 2020
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COVID-19 and the risk to health care workers: a case report
Ng, K., Poon, B.H., Kiat Puar, T.H., Shan Quah, J.L., Loh, W.J., Wong, Y.J., Tan, T.Y., Raghuram, J., 2020 · 2020
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I., 2017 · 2017
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.W., Lee, K., Toutanova, K., 2018 · 2018
Cited alongside, same era.
Understanding individual decisions of cnns via contrastive backpropagation, in: Asian Conference on Computer Vision, Springer. pp. 119–134
Gu, J., Yang, Y., Tresp, V., 2018 · 2018
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Improving language understanding by generative pre-training
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., 2018 · 2018
Cited alongside, same era.
Severity scoring of lung oedema on the chest radiograph is associated with clinical outcomes in ARDS
Warren, M.A., Zhao, Z., Koyama, T., Bastarache, J.A., Shaver, C.M., Semler, M.W., Rice, T.W., Matthay, M.A., Calfee, C.S., Ware, L.B., 2018 · 2018
Cited alongside, same era.
Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study
Zech, J.R., Badgeley, M.A., Liu, M., Costa, A.B., Titano, J.J., Oermann, E.K., 2018 · 2018
Cited alongside, same era.
Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in china: a report of 1014 cases
Ai, T., Yang, Z., Hou, H., Zhan, C., Chen, C., Lv, W., Tao, Q., Sun, Z., Xia, L., 2020 · 2019
Cited alongside, same era.
Chest x-ray in new coronavirus disease 2019 (COVID-19) infection: findings and correlation with clinical outcome
Cozzi, D., Albanesi, M., Cavigli, E., Moroni, C., Bindi, A., Luvarà, S., Lucarini, S., Busoni, S., Mazzoni, L.N., Miele, V., 2020 · 2019
Cited alongside, same era.
Deep learning COVID-19 features on cxr using limited training data sets
Oh, Y., Park, S., Ye, J.C., 2020 · 2020
Later among the works it cites.
Radiological findings from 81 patients with COVID-19 pneumonia in wuhan, china: a descriptive study
Shi, H., Han, X., Jiang, N., Cao, Y., Alwalid, O., Gu, J., Fan, Y., Zheng, C., 2020 · 2020
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Real-time rt-pcr in COVID-19 detection: issues affecting the results
Tahamtan, A., Ardebili, A., 2020 · 2020
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Clinical and chest radiography features determine patient outcomes in young and middle-aged 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., et al., 2020 · 2020
Later among the works it cites.
Transformers in vision: A survey
Khan, S., Naseer, M., Hayat, M., Zamir, S.W., Khan, F.S., Shah, M., 2021 · 2021
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Oh, Y., Ye, J.C., 2021 · 2021
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Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
Roberts, M., Driggs, D., Thorpe, M., Gilbey, J., Yeung, M., Ursprung, S., Aviles-Rivero, A.I., Etmann, C., McCague, C., Beer, L., et al., 2021 · 2021
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Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2097–2106
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M., 2017 · 2097
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