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Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis.
I. Sluimer, A. Schilham, M. Prokop, and B. Van Ginneken, “Computer analysis of computed tomography scans of the lung: a survey,” IEEE Transactions on Medical Imaging , vol. 25, no. 4, pp. 385–405, 2006
2006
Earlier work this paper cites.
M. Keshani, Z. Azimifar, F. Tajeripour, and R. Boostani, “Lung nodule segmentation and recognition using SVM classifier and active contour modeling: A complete intelligent system,” Computers in Biology and Medicine , vol. 43, no. 4, pp. 287–300, 2013
2013
Earlier work this paper cites.
D.-H. Lee, “Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks,” in Workshop on challenges in representation learning, ICML , vol. 3, 2013, p. 2
2013
Earlier work this paper cites.
S. Shen, A. A. Bui, J. Cong, and W. Hsu, “An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy,” Computers in Biology and Medicine , vol. 57, pp. 139–149, 2015
2015
Earlier work this paper cites.
A. Rasmus, M. Berglund, M. Honkala, H. Valpola, and T. Raiko, “Semi-supervised learning with ladder networks,” in NIPS , 2015, pp. 3546–3554
2015
Earlier work this paper cites.
C. Szegedy, W. Liu et al. , “Going deeper with convolutions,” in CVPR , 2015, pp. 1–9
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” in MICCAI . Springer, 2015, pp. 234–241
2015
Earlier work this paper cites.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in CVPR , 2015, pp. 3431–3440
2015
Earlier work this paper cites.
H. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Transactions on Medical Imaging , vol. 35, no. 5, pp. 1285–1298, 2016
2016
Earlier work this paper cites.
L. Chen, H. Qu, J. Zhao, B. Chen, and J. C. Principe, “Efficient and robust deep learning with correntropy-induced loss function,” Neural Computing and Applications , vol. 27, no. 4, pp. 1019–1031, 2016
2016
Earlier work this paper cites.
S. Wang, M. Zhou et al. , “Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation,” Medical Image Analysis , vol. 40, pp. 172–183, 2017
2017
Earlier work this paper cites.
T. Schlegl, P. Seeböck et al. , “Unsupervised anomaly detection with generative adversarial networks to guide marker discovery,” in Information Processing in Medical Imaging , Cham, 2017, pp. 146–157
2017
Earlier work this paper cites.
S. Laine and T. Aila, “Temporal ensembling for semi-supervised learning,” ICLR , 2017
2017
Earlier work this paper cites.
Y. Wei, J. Feng, X. Liang, M.-M. Cheng, Y. Zhao, and S. Yan, “Object region mining with adversarial erasing: A simple classification to semantic segmentation approach,” in CVPR , 2017, pp. 1568–1576
2017
Earlier work this paper cites.
D.-P. Fan, M.-M. Cheng, Y. Liu, T. Li, and A. Borji, “Structure-measure: A new way to evaluate foreground maps,” in ICCV , 2017, pp. 4548–4557
2017
Earlier work this paper cites.
P. M. Gordaliza, A. Muñoz-Barrutia, M. Abella, M. Desco, S. Sharpe, and J. J. Vaquero, “Unsupervised CT lung image segmentation of a mycobacterium tuberculosis infection model,” Scientific reports , vol. 8, no. 1, pp. 1–10, 2018
2018
Earlier work this paper cites.
D. Jin, Z. Xu, Y. Tang, A. P. Harrison, and D. J. Mollura, “CT-realistic lung nodule simulation from 3D conditional generative adversarial networks for robust lung segmentation,” in MICCAI . Springer, 2018, pp. 732–740
2018
Earlier work this paper cites.
J. Jiang, Y.-C. Hu et al. , “Multiple resolution residually connected feature streams for automatic lung tumor segmentation from CT images,” IEEE Transactions on Medical Imaging , vol. 38, no. 1, pp. 134–144, 2018
2018
Earlier work this paper cites.
D. Nie, Y. Gao, L. Wang, and D. Shen, “Asdnet: Attention based semi-supervised deep networks for medical image segmentation,” in MICCAI . Springer, 2018, pp. 370–378
2018
Earlier work this paper cites.
H. Fu, J. Cheng, Y. Xu, D. W. K. Wong, J. Liu, and X. Cao, “Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation,” IEEE Transactions on Medical Imaging , vol. 37, no. 7, pp. 1597–1605, jul 2018
2018
Earlier work this paper cites.
S. Chen, X. Tan, B. Wang, and X. Hu, “Reverse attention for salient object detection,” in ECCV , 2018, pp. 234–250
2018
Earlier work this paper cites.
C. Liangjun, P. Honeine, Q. Hua, Z. Jihong, and S. Xia, “Correntropy-based robust multilayer extreme learning machines,” Pattern Recognition , vol. 84, pp. 357–370, 2018
2018
Earlier work this paper cites.
O. Oktay, J. Schlemper et al. , “Attention U-Net: Learning Where to Look for the Pancreas,” in International Conference on Medical Imaging with Deep Learning , 2018
2018
Earlier work this paper cites.
X. Li, H. Chen, X. Qi, Q. Dou, C. Fu, and P. Heng, “H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes,” IEEE Transactions on Medical Imaging , vol. 37, no. 12, pp. 2663–2674, 2018
2018
Earlier work this paper cites.
L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-decoder with atrous separable convolution for semantic image segmentation,” in ECCV , 2018, pp. 801–818
2018
Earlier work this paper cites.
D.-P. Fan, C. Gong, Y. Cao, B. Ren, M.-M. Cheng, and A. Borji, “Enhanced-alignment measure for binary foreground map evaluation,” IJCAI , pp. 698–704, 2018
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
V. Cheplygina, M. de Bruijne, and J. P. Pluim, “Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis,” Medical Image Analysis , vol. 54, pp. 280–296, 2019
2019
Earlier work this paper cites.
W. Cui, Y. Liu et al. , “Semi-supervised brain lesion segmentation with an adapted mean teacher model,” in Information Processing in Medical Imaging , 2019, pp. 554–565
2019
Cited alongside, same era.
Y.-X. Zhao, Y.-M. Zhang, M. Song, and C.-L. Liu, “Multi-view Semi-supervised 3D Whole Brain Segmentation with a Self-ensemble Network,” in MICCAI , 2019, pp. 256–265
2019
Cited alongside, same era.
Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh, and J. Liang, “UNet++: A nested U-Net architecture for medical image segmentation,” IEEE Transactions on Medical Imaging , pp. 3–11, 2019
2019
Cited alongside, same era.
J.-X. Zhao, J.-J. Liu, D.-P. Fan, Y. Cao, J. Yang, and M.-M. Cheng, “EGNet: Edge guidance network for salient object detection,” in ICCV , 2019, pp. 8779–8788
2019
Cited alongside, same era.
Z. Wu, L. Su, and Q. Huang, “Stacked cross refinement network for edge-aware salient object detection,” in ICCV , 2019, pp. 7264–7273
L. Wang and A. Wong, “COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest Radiography Images,” arXiv , mar 2020
2020
Closest in time.
J. Zhang, Y. Xie, Y. Li, C. Shen, and Y. Xia, “COVID-19 Screening on Chest X-ray Images Using Deep Learning based Anomaly Detection,” arXiv , mar 2020
2020
Closest in time.
X. Xu, X. Jiang et al. , “Deep learning system to screen coronavirus disease 2019 pneumonia,” arXiv , 2020
2020
Closest in time.
C. Zheng, X. Deng et al. , “Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label,” medRxiv , 2020
2020
Closest in time.
S. Chaganti, A. Balachandran et al. , “Quantification of tomographic patterns associated with COVID-19 from chest CT,” arXiv , 2020
2020
Closest in time.
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2019
Cited alongside, same era.
Z. Zhang, H. Fu, H. Dai, J. Shen, Y. Pang, and L. Shao, “ET-Net: A generic edge-attention guidance network for medical image segmentation,” in MICCAI , 2019, pp. 442–450
2019
Cited alongside, same era.
Z. Gu, J. Cheng et al. , “CE-Net: Context Encoder Network for 2D Medical Image Segmentation,” IEEE Transactions on Medical Imaging , vol. 38, no. 10, pp. 2281–2292, 2019
2019
Cited alongside, same era.
S. Zhang, H. Fu et al. , “Attention Guided Network for Retinal Image Segmentation,” in MICCAI , 2019, pp. 797–805
2019
Cited alongside, same era.
Z. Wu, L. Su, and Q. Huang, “Cascaded partial decoder for fast and accurate salient object detection,” in CVPR , 2019, pp. 3907–3916
2019
Cited alongside, same era.
S. Gao, M.-M. Cheng, K. Zhao, X.-Y. Zhang, M.-H. Yang, and P. H. Torr, “Res2Net: A new multi-scale backbone architecture,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2019
2019
Cited alongside, same era.
X. Qin, Z. Zhang, C. Huang, C. Gao, M. Dehghan, and M. Jagersand, “BASNet: Boundary-aware salient object detection,” in CVPR , 2019, pp. 7479–7489
2019
Cited alongside, same era.
S. Mittal, M. Tatarchenko, Ö. Çiçek, and T. Brox, “Parting with illusions about deep active learning,” arXiv , 2019
2019
Cited alongside, same era.
F. Shan, Y. Gao et al. , “Lung infection quantification of COVID-19 in CT images with deep learning,” arXiv , 2020
2020
Closest in time.
B. Kamble, S. P. Sahu, and R. Doriya, “A review on lung and nodule segmentation techniques,” in Advances in Data and Information Sciences . Springer, 2020, pp. 555–565
2020
Closest in time.
Y.-H. Wu, S.-H. Gao et al. , “JCS: An explainable covid-19 diagnosis system by joint classification and segmentation,” arXiv , 2020
2020
Closest in time.
K. Zhou, S. Gao et al. , “Sparse-GAN: Sparsity-constrained Generative Adversarial Network for Anomaly Detection in Retinal OCT Image,” in ISBI , 2020
2020
Closest in time.
J. E. van Engelen and H. H. Hoos, “A survey on semi-supervised learning,” Machine Learning , vol. 109, no. 2, pp. 373–440, feb 2020
2020
Closest in time.
N. Tajbakhsh, L. Jeyaseelan, Q. Li, J. N. Chiang, Z. Wu, and X. Ding, “Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation,” Medical Image Analysis , vol. 63, p. 101693, 2020
2020
Closest in time.
D. Dong, Z. Tang et al. , “The role of imaging in the detection and management of COVID-19: a review,” IEEE Reviews in Biomedical Engineering , 2020
2020
Closest in time.
H. Kang, L. Xia et al. , “Diagnosis of coronavirus disease 2019 (covid-19) with structured latent multi-view representation learning,” arXiv , 2020
2020
Closest in time.
Y. Oh, S. Park, and J. C. Ye, “Deep learning covid-19 features on cxr using limited training data sets,” arXiv , 2020
2020
Closest in time.
S. Wang, B. Kang et al. , “A deep learning algorithm using CT images to screen for corona virus disease (COVID-19),” medRxiv , 2020
2020
Closest in time.
J. Chen, L. Wu et al. , “Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study,” medRxiv , 2020
2020
Closest in time.
A. W. Senior, R. Evans et al. , “Improved protein structure prediction using potentials from deep learning,” Nature , vol. 577, no. 7792, pp. 706–710, jan 2020
2020
Closest in time.
Z. Hu, Q. Ge, L. Jin, and M. Xiong, “Artificial intelligence forecasting of COVID-19 in China,” arXiv , 2020
2020
Closest in time.
O. Gozes, M. Frid-Adar et al. , “Rapid AI development cycle for the coronavirus (COVID-19) pandemic: Initial results for automated detection & patient monitoring using deep learning CT image analysis,” arXiv , 2020
2020
Closest in time.
Z. Tang, W. Zhao et al. , “Severity assessment of coronavirus disease 2019 (COVID-19) using quantitative features from chest CT images,” arXiv , 2020
2020
Closest in time.
F. Shi, L. Xia et al. , “Large-scale screening of COVID-19 from community acquired pneumonia using infection size-aware classification,” arXiv , 2020
2020
Closest in time.
F. Isensee, P. F. Jäger, S. A. A. Kohl, J. Petersen, and K. H. Maier-Hein, “Automated Design of Deep Learning Methods for Biomedical Image Segmentation,” arXiv , 2020
2020
Closest in time.
J. Wei, S. Wang, and Q. Huang, “F3Net: Fusion, feedback and focus for salient object detection,” in AAAI , 2020
2020
Closest in time.
T. Zhou, H. Fu, G. Chen, J. Shen, and L. Shao, “Hi-net: hybrid-fusion network for multi-modal MR image synthesis,” IEEE Transactions on Medical Imaging , 2020
2020
Closest in time.
J. Zhang, D.-P. Fan et al. , “UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders,” in CVPR , 2020
2020
Closest in time.
D.-P. Fan, G.-P. Ji, T. Zhou, G. Chen, H. Fu, J. Shen, and L. Shao, “PraNet: Parallel Reverse Attention Network for Polyp Segmentation,” arXiv , 2020
2020
Closest in time.
D.-P. Fan, G.-P. Ji, G. Sun, M.-M. Cheng, J. Shen, and L. Shao, “Camouflaged object detection,” in CVPR , 2020
2020
Closest in time.
Y. Zhou, X. He, L. Huang, L. Liu, F. Zhu, S. Cui, and L. Shao, “Collaborative learning of semi-supervised segmentation and classification for medical images,” in CVPR , 2019, pp. 2079–2088
2088
Closest in time.