The DOI matches its Crossref record; 87 of the 87 references it cites were found in a registry or an index.
Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images
Open accessAbstract
Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis. Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. However, segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. Further, collecting a large amount of data is impractical within a short time period, inhibiting the training of a deep model. To address these challenges, a novel COVID-19 Lung Infection Segmentation Deep Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices. In our Inf-Net, a parallel partial decoder is used to aggregate the high-level features and generate a global map. Then, the implicit reverse attention and explicit edge-attention are utilized to model the boundaries and enhance the representations. Moreover, to alleviate the shortage of labeled data, we present a semi-supervised segmentation framework based on a randomly selected propagation strategy, which only requires a few labeled images and leverages primarily unlabeled data. Our semi-supervised framework can improve the learning ability and achieve a higher performance. Extensive experiments on our COVID-SemiSeg and real CT volumes demonstrate that the proposed Inf-Net outperforms most cutting-edge segmentation models and advances the state-of-the-art performance.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30183-5 · Cited by 48,884
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_49 · Cited by 20,054
nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein, K. H. (2021) · Nature Methods · doi:10.1038/s41592-020-01008-z · Cited by 10,477
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
Zhou, Z., Rahman Siddiquee, M. M., Tajbakhsh, N., & Liang, J. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-00889-5_1 · Cited by 9,610
A novel coronavirus outbreak of global health concern
Wang, C., Horby, P. W., Hayden, F. G., & Gao, G. F. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30185-9 · Cited by 7,700
Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759
Ai, T., Yang, Z., Hou, H., Zhan, C., Chen, C., Lv, W., Tao, Q., Sun, Z., & Xia, L. (2020) · Radiology · doi:10.1148/radiol.2020200642 · Cited by 5,685
Attention U-Net: Learning Where to Look for the Pancreas
Oktay, O., Schlemper, J., Folgoc, L. L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N. Y., Kainz, B., et al. (2018) · arXiv · doi:10.48550/arxiv.1804.03999 · Cited by 5,458
Semi-Supervised Learning Literature Survey
Zhu, X. (2005) · Cited by 3,844
Res2Net: A New Multi-Scale Backbone Architecture
Gao, S.-H., Cheng, M.-M., Zhao, K., Zhang, X.-Y., Yang, M.-H., & Torr, P. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2938758 · Cited by 3,385
Improved protein structure prediction using potentials from deep learning
Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A. W. R., Bridgland, A., et al. (2020) · Nature · doi:10.1038/s41586-019-1923-7 · Cited by 3,378
Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR
Fang, Y., Zhang, H., Xie, J., Lin, M., Ying, L., Pang, P., & Ji, W. (2020) · Radiology · doi:10.1148/radiol.2020200432 · Cited by 3,354
Wang, L., Lin, Z. Q., & Wong, A. (2020) · Scientific Reports · doi:10.1038/s41598-020-76550-z · Cited by 3,113
A survey on semi-supervised learning
van Engelen, J. E., & Hoos, H. H. (2020) · Machine Learning · doi:10.1007/s10994-019-05855-6 · Cited by 2,666
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
Schlegl, T., Seeböck, P., Waldstein, S. M., Schmidt-Erfurth, U., & Langs, G. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-59050-9_12 · Cited by 2,627
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes
Li, X., Chen, H., Qi, X., Dou, Q., Fu, C.-W., & Heng, P.-A. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2845918 · Cited by 2,473
CE-Net: Context Encoder Network for 2D Medical Image Segmentation
Gu, Z., Cheng, J., Fu, H., Zhou, K., Hao, H., Zhao, Y., Zhang, T., Gao, S., & Liu, J. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2903562 · Cited by 2,267
Self-citationStructure-Measure: A New Way to Evaluate Foreground Maps
Fan, D.-P., Cheng, M.-M., Liu, Y., Li, T., & Borji, A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.487 · Cited by 2,092
Self-citationAttention gated networks: learning to leverage salient regions in medical images
Schlemper, J., Oktay, O., Schaap, M., Heinrich, M., Kainz, B., Glocker, B., & Rueckert, D. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.01.012 · Cited by 2,014
Can AI help in screening viral and COVID-19 pneumonia?
Chowdhury, M. E. H., Rahman, T., Khandakar, A., Mazhar, R., Kadir, M. A., Mahbub, Z. B., Islam, K. R., Khan, M. S., Iqbal, A., Emadi, N. A., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3010287 · Cited by 1,913
A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)
Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., Cai, M., Yang, J., Li, Y., Meng, X., et al. (2021) · European Radiology · doi:10.1007/s00330-021-07715-1 · Cited by 1,677
Enhanced-alignment Measure for Binary Foreground Map Evaluation
Fan, D.-P., Gong, C., Cao, Y., Ren, B., Cheng, M.-M., & Borji, A. (2018) · Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/97 · Cited by 1,607
Self-citationBASNet: Boundary-Aware Salient Object Detection
Qin, X., Zhang, Z., Huang, C., Gao, C., Dehghan, M., & Jagersand, M. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00766 · Cited by 1,565
PraNet: Parallel Reverse Attention Network for Polyp Segmentation
Fan, D.-P., Ji, G.-P., Zhou, T., Chen, G., Fu, H., Shen, J., & Shao, L. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-59725-2_26 · Cited by 1,499
Self-citationShi, F., Wang, J., Shi, J., Wu, Z., Wang, Q., Tang, Z., He, K., Shi, Y., & Shen, D. (2021) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2020.2987975 · Cited by 1,404
Temporal Ensembling for Semi-Supervised Learning
Laine, S., & Aila, T. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.02242 · Cited by 1,395
Chest CT manifestations of new coronavirus disease 2019 (COVID-19): a pictorial review
Ye, Z., Zhang, Y., Wang, Y., Huang, Z., & Song, B. (2020) · European Radiology · doi:10.1007/s00330-020-06801-0 · Cited by 1,349
The Role of Chest Imaging in Patient Management During the COVID-19 Pandemic
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., et al. (2020) · Chest · doi:10.1016/j.chest.2020.04.003 · Cited by 1,245
A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT
Wang, X., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H., Liu, W., & Zheng, C. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2995965 · Cited by 1,243
Deep Learning for Anomaly Detection: A Survey
Chalapathy, R., & Chawla, S. (2019) · arXiv · doi:10.48550/arxiv.1901.03407 · Cited by 1,240
A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia
Xu, X., Jiang, X., Ma, C., Du, P., Li, X., Lv, S., Yu, L., Ni, Q., Chen, Y., Su, J., et al. (2020) · Engineering · doi:10.1016/j.eng.2020.04.010 · Cited by 1,209
EGNet: Edge Guidance Network for Salient Object Detection
Zhao, J., Liu, J.-J., Fan, D.-P., Cao, Y., Yang, J., & Cheng, M.-M. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00887 · Cited by 1,202
Self-citationImaging Profile of the COVID-19 Infection: Radiologic Findings and Literature Review
Ng, M.-Y., Lee, E. Y. P., Yang, J., Yang, F., Li, X., Wang, H., Lui, M. M.-S., Lo, C. S.-Y., Leung, B., Khong, P.-L., et al. (2020) · Radiology: Cardiothoracic Imaging · doi:10.1148/ryct.2020200034 · Cited by 1,140
Cascaded Partial Decoder for Fast and Accurate Salient Object Detection
Wu, Z., Su, L., & Huang, Q. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00403 · Cited by 1,136
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., et al. (2020) · Radiology · doi:10.1148/radiol.2020201365 · Cited by 1,101
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) · Medical Image Analysis · doi:10.1016/j.media.2020.101693 · Cited by 1,095
F³Net: Fusion, Feedback and Focus for Salient Object Detection
Wei, J., Wang, S., & Huang, Q. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6916 · Cited by 1,050
Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images
Song, Y., Zheng, S., Li, L., Zhang, X., Zhang, X., Huang, Z., Chen, J., Wang, R., Zhao, H., Chong, Y., et al. (2021) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3065361 · Cited by 957
Self-citationDeep Learning COVID-19 Features on CXR Using Limited Training Data Sets
Oh, Y., Park, S., & Ye, J. C. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2993291 · Cited by 897
Fan, D.-P., Ji, G.-P., Sun, G., Cheng, M.-M., Shen, J., & Shao, L. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00285 · Cited by 896
Self-citationWei, Y., Feng, J., Liang, X., Cheng, M.-M., Zhao, Y., & Yan, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.687 · Cited by 863
Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation
Fu, H., Cheng, J., Xu, Y., Wong, D. W. K., Liu, J., & Cao, X. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2791488 · Cited by 834
Self-citationCOVID-19 Image Data Collection
Cohen, J., Morrison, P., & Dao, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.11597 · Cited by 722
Reverse Attention for Salient Object Detection
Chen, S., Tan, X., Wang, B., & Hu, X. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01240-3_15 · Cited by 684
Chen, J., Wu, L., Zhang, J., Zhang, L., Gong, D., Zhao, Y., Chen, Q., Huang, S., Yang, M., Yang, X., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-76282-0 · Cited by 675
COVID-CT-Dataset: A CT Scan Dataset about COVID-19
Yang, X., He, X., Zhao, J., Zhang, Y., Zhang, S., & Xie, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.13865 · Cited by 673
Gozes, O., Frid-Adar, M., Greenspan, H. K., Browning, P. D., Zhang, H., Ji, W., Bernheim, A., & Siegel, E. L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.05037 · Cited by 639
Computer analysis of computed tomography scans of the lung: a survey
Sluimer, I., Schilham, A., Prokop, M., & van Ginneken, B. (2006) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2005.862753 · Cited by 556
Semi-Supervised Learning with Ladder Networks
Rasmus, A., Harri Valpola, B., Honkala, M., Berglund, M., & Raiko, T. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1507.02672 · Cited by 527
Lung Infection Quantification of COVID-19 in CT Images with Deep Learning
Shan, F., Gao, Y., Wang, J., Shi, W., Shi, N., Han, M., Xue, Z., Shen, D. G., & Shi, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.04655 · Cited by 490
Wang, S., Zhou, M., Liu, Z., Liu, Z., Gu, D., Zang, Y., Dong, D., Gevaert, O., & Tian, J. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.06.014 · Cited by 478
Stacked Cross Refinement Network for Edge-Aware Salient Object Detection
Wu, Z., Su, L., & Huang, Q. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00736 · Cited by 448
UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders
Zhang, J., Fan, D.-P., Dai, Y., Anwar, S., Saleh, F. S., Zhang, T., & Barnes, N. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00861 · Cited by 423
Self-citationJCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation
Wu, Y.-H., Gao, S.-H., Mei, J., Xu, J., Fan, D.-P., Zhang, R.-G., & Cheng, M.-M. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3058783 · Cited by 403
Self-citationThe Role of Imaging in the Detection and Management of COVID-19: A Review
Dong, D., Tang, Z., Wang, S., Hui, H., Gong, L., Lu, Y., Xue, Z., Liao, H., Chen, F., Yang, F., et al. (2021) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2020.2990959 · Cited by 401
Hi-Net: Hybrid-Fusion Network for Multi-Modal MR Image Synthesis
Zhou, T., Fu, H., Chen, G., Shen, J., & Shao, L. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2975344 · Cited by 399
Self-citationCOVID-19 Screening on Chest X-ray Images Using Deep Learning based Anomaly Detection
Zhang, J., Xie, Y., Li, Y., Shen, C., & Xia, Y. (2020) · Europe PMC (PubMed Central) · Cited by 394
Artificial Intelligence Forecasting of Covid-19 in China
Hu, Z., Ge, Q., Li, S., & Xiong, M. (2020) · International Journal of Educational Excellence · doi:10.18562/ijee.054 · Cited by 351
Gunraj, H., Wang, L., & Wong, A. (2020) · Frontiers in Medicine · doi:10.3389/fmed.2020.608525 · Cited by 325
ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image Segmentation
Zhang, Z., Fu, H., Dai, H., Shen, J., Pang, Y., & Shao, L. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32239-7_49 · Cited by 297
Self-citationCollaborative Learning of Semi-Supervised Segmentation and Classification for Medical Images
Zhou, Y., He, X., Huang, L., Liu, L., Zhu, F., Cui, S., & Shao, L. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00218 · Cited by 285
Self-citationASDNet: Attention Based Semi-supervised Deep Networks for Medical Image Segmentation
Nie, D., Gao, Y., Wang, L., & Shen, D. G. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00937-3_43 · Cited by 280
Jiang, J., Hu, Y.-C., Liu, C.-J., Halpenny, D., Hellmann, M. D., Deasy, J. O., Mageras, G., & Veeraraghavan, H. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2857800 · Cited by 268
Kang, H., Xia, L., Yan, F., Wan, Z., Shi, F., Yuan, H., Jiang, H., Wu, D., Sui, H., Zhang, C., et al. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2992546 · Cited by 247
Shi, F., Xia, L., Shan, F., Song, B., Wu, D., Wei, Y., Yuan, H., Jiang, H., He, Y., Gao, Y., et al. (2021) · Physics in Medicine & Biology · doi:10.1088/1361-6560/abe838 · Cited by 239
Semi-supervised Brain Lesion Segmentation with an Adapted Mean Teacher Model
Cui, W., Liu, Y., Li, Y., Guo, M.-H., Li, Y., Li, X., Wang, T., Zeng, X., & Ye, C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20351-1_43 · Cited by 223
Attention Guided Network for Retinal Image Segmentation
Zhang, S., Fu, H., Yan, Y., Zhang, Y., Wu, Q., Yang, M., Tan, M., & Xu, Y. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-32239-7_88 · Cited by 201
Self-citationJin, D., Xu, Z., Tang, Y., Harrison, A. P., & Mollura, D. J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00934-2_81 · Cited by 190
Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT
Chaganti, S., Greniér, P., Balachandran, A., Chabin, G., Cohen, S. L., Flohr, T. G., Georgescu, B., Grbić, S., Liu, S., Mellot, F., et al. (2020) · Radiology Artificial Intelligence · doi:10.1148/ryai.2020200048 · Cited by 163
Tang, Z., Zhao, W., Xie, X., Zheng, Z., Shi, F., Liu, J., & Shen, D. G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.11988 · Cited by 132
Rajinikanth, V., Dey, N., Raj, A. N. J., Hassanien, A. E., Santosh, K., & Raja, N. S. M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.03431 · Cited by 123
Shen, S., Bui, A. A. T., Cong, J., & Hsu, W. W. Y. (2014) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2014.12.008 · Cited by 105
Efficient and robust deep learning with Correntropy-induced loss function
Chen, L., Qu, H., Zhao, J., Chen, B., & Príncipe, J. C. (2015) · Neural Computing and Applications · doi:10.1007/s00521-015-1916-x · Cited by 95
Zhou, K. Q., Gao, S., Cheng, J. T., Gu, Z., Fu, H., Tu, Z., Yang, J., Zhao, Y., & Liu, J. (2020) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi45749.2020.9098374 · Cited by 91
Self-citationAutomated Design of Deep Learning Methods for Biomedical Image SegmentationPreprint
Isensee, F., Jäger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein, K. H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.08128 · Cited by 90
Parting with Illusions about Deep Active LearningPreprint
Mittal, S., Tatarchenko, M., Çiçek, Ö., & Brox, T. (2019) · arXiv.org · doi:10.48550/arxiv.1912.05361 · Cited by 75
Towards Efficient COVID-19 CT Annotation: A Benchmark for Lung and Infection SegmentationPreprint
Ma, J., Wang, Y., An, X., Ge, C., Yu, Z., Chen, J., Zhu, Q., Dong, G., He, J., He, Z., et al. (2020) · arXiv (Cornell University) · Cited by 74
Shi, F., Xia, L., Shan, F., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.09860 · Cited by 63
Quantification of Lung Damage in an Elastase-Induced Mouse Model of Emphysema
Muñoz‐Barrutia, A., Ceresa, M., Artaechevarria, X., Montuenga, L. M., & Ortíz-de-Solórzano, C. (2012) · International Journal of Biomedical Imaging · doi:10.1155/2012/734734 · Cited by 60
Correntropy-based robust multilayer extreme learning machines
Chen, L., Honeiné, P., Qu, H., Zhao, J., & Sun, X. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.07.011 · Cited by 48
Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT.Preprint
Chaganti, S., Balachandran, A., Chabin, G., Cohen, S. L., Flohr, T. G., Georgescu, B., Greniér, P., Grbić, S., Liu, S., Mellot, F., et al. (2020) · PubMed · Cited by 48
Unsupervised CT Lung Image Segmentation of a Mycobacterium Tuberculosis Infection Model
Gordaliza, P. M., Muñoz‐Barrutia, A., Abella, M., Desco, M., Sharpe, S. A., & Vaquero, J. J. (2018) · Scientific Reports · doi:10.1038/s41598-018-28100-x · Cited by 43
Multi-view Semi-supervised 3D Whole Brain Segmentation with a Self-ensemble Network
Zhao, Y., Zhang, Y., Song, M., & Liu, C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32248-9_29 · Cited by 35
A Review on Lung and Nodule Segmentation Techniques
Kamble, B., Sahu, S. P., & Doriya, R. (2020) · Lecture notes in networks and systems · doi:10.1007/978-981-15-0694-9_52 · Cited by 24
Cited by
RGB-D salient object detection: A survey
Zhou, T., Fan, D.-P., Cheng, M.-M., Shen, J., & Shao, L. (2021) · Computational Visual Media · doi:10.1007/s41095-020-0199-z · Cited by 331
Self-citationDeep learning based multimodal biomedical data fusion: an overview and comparative review
Duan, J., Xiong, J., Li, Y., & Ding, W. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102536 · Cited by 284
Yang, D., Xu, Z., Li, W., Myronenko, A., Roth, H. R., Harmon, S., Xu, S., Turkbey, B., Turkbey, E., Wang, X., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.101992 · Cited by 279
Applications of artificial intelligence in battling against covid-19: A literature review
Tayarani N., M.-H. (2021) · Chaos, Solitons & Fractals · doi:10.1016/j.chaos.2020.110338 · Cited by 210
Computer-aided detection of COVID-19 from X-ray images using multi-CNN and Bayesnet classifier
Abraham, B., & Nair, M. S. (2020) · Biocybernetics and Biomedical Engineering · doi:10.1016/j.bbe.2020.08.005 · Cited by 169
Metadata
- Authors
- Fan, D.-P., Zhou, T., Ji, G.-P., Zhou, Y., Chen, G., Fu, H., Shen, J., & Shao, L.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2020-08
- Volume
- 39
- Issue
- 8
- Pages
- 2626-2637
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W3027763298
- Cited by
- 1,259
- References
- 87
- FWCI
- 97.27
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
