The DOI matches its Crossref record; 42 of the 45 references it cites were found in a registry or an index.
Fetal Congenital Heart Disease Echocardiogram Screening Based on DGACNN: Adversarial One-Class Classification Combined with Video Transfer Learning
Abstract
Fetal congenital heart disease (FHD) is a common and serious congenital malformation in children. In Asia, FHD birth defect rates have reached as high as 9.3%. For the early detection of birth defects and mortality, echocardiography remains the most effective method for screening fetal heart malformations. However, standard echocardiograms of the fetal heart, especially four-chamber view images, are difficult to obtain. In addition, the pathophysiological changes in fetal hearts during different pregnancy periods lead to ever-changing two-dimensional fetal heart structures and hemodynamics, and it requires extensive professional knowledge to recognize and judge disease development. Thus, research on the automatic screening for FHD is necessary. In this paper, we proposed a new model named DGACNN that shows the best performance in recognizing FHD, achieving a rate of 85%. The motivation for this network is to deal with the problem that there are insufficient training datasets to train a robust model. There are many unlabeled video slices, but they are tough and time-consuming to annotate. Thus, how to use these un-annotated video slices to improve the DGACNN capability for recognizing FHD, in terms of both recognition accuracy and robustness, is very meaningful for FHD screening. The architecture of DGACNN comprises two parts, that is, DANomaly and GACNN (Wgan-GP and CNN). DANomaly, similar to the ALOCC network, but incorporates cycle adversarial learning to train an end-to-end one-class classification (OCC) network that is more robust and has a higher accuracy than ALOCC in screening video slices. For the GACNN architecture, we use FCH (four chamber heart) video slices at around the end-systole, as screened by DANomaly, to train a WGAN-GP for the purpose of obtaining ideal low-level features that can robustly improve the FHD recognition accuracy. A few annotated video slices, as screened by DANomaly, can also be used for data augmentation so as to improve the FHD recognition further. The experiments show that the DGACNN outperforms other state-of-the-art networks by 1%-20% in recognizing FHD. A comparison experiment shows that the proposed network already outperforms the performance of expert cardiologists in recognizing FHD, reaching 84% in a test. Thus, the proposed architecture has high potential for helping cardiologists complete early FHD screenings.
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In the citing paper
“CHD was effectively detected by AI models generated by the research groups of Gong et al. and Nurmaini et al. [61,62], and selectively for the diagnosis of total anomalous pulmonary venous connection by Wang et al. [63].”
In the citing paper
“First, most of the studies only used 4CV images as the input for their algorithms [55,57,61,62,63,67,70]”
In the citing paper
“Advantages are the facilitation of standard view acquisition [58,60] and CHD detection [53,61,62], as well as a significant reduction in examination time [52,55].”
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
Image-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930
Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,119
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408
Improved training of Wasserstein GANs
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., & Courville, A. (2017) · arXiv · doi:10.48550/arxiv.1704.00028 · Cited by 6,800
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
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636
Image information and visual quality
Sheikh, H. R., & Bovik, A. C. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.859378 · Cited by 4,104
Birth prevalence of congenital heart disease worldwide
van der Linde, D., Konings, E. E. M., Slager, M. A., Witsenburg, M., Helbing, W. A., Takkenberg, J. J. M., & Roos-Hesselink, J. W. (2011) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2011.08.025 · Cited by 3,452
(2007) · CaltechAUTHORS (California Institute of Technology) · Cited by 2,274
Generative adversarial network in medical imaging: A review
Xin, Y., Walia, E., & Babyn, P. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101552 · Cited by 1,959
Go, A. S., Mozaffarian, D., Roger, V. L., Benjamin, E. J., Berry, J. D., Borden, W. B., Bravata, D. M., Dai, S., Ford, E. S., Fox, C. S., et al. (2013) · Circulation · doi:10.1161/cir.0b013e318282ab8f · Cited by 1,294
Congenital Heart Defects in Europe
Dolk, H. M., Loane, M. A., Garne, E., & a European Surveillance of Congenital Anomalies (EUROCAT) Working Group (2011) · Circulation · doi:10.1161/circulationaha.110.958405 · Cited by 962
Adversarially Learned One-Class Classifier for Novelty Detection
Sabokrou, M., Khalooei, M., Fathy, M., & Adeli, E. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00356 · Cited by 765
Gilboa, S. M., Salemi, J. L., Nembhard, W. N., Fixler, D. E., & Correa, A. (2010) · Circulation · doi:10.1161/circulationaha.110.947002 · Cited by 610
Modeling, Clustering, and Segmenting Video with Mixtures of Dynamic Textures
Chan, A. B., & Vasconcelos, N. M. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.70738 · Cited by 460
Prenatal diagnosis of coarctation of the aorta improves survival and reduces morbidity: Table 1
Franklin, O., Burch, M., Manning, N., Sleeman, K., Gould, S., & Archer, N. (2002) · Heart · doi:10.1136/heart.87.1.67 · Cited by 419
Lung cancer classification using neural networks for CT images
Kuruvilla, J., & Gunavathi, K. (2013) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2013.10.011 · Cited by 366
Sharpness-Aware Low-Dose CT Denoising Using Conditional Generative Adversarial Network
Xin, Y., & Babyn, P. (2018) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-018-0056-0 · Cited by 344
Multisource Transfer Learning With Convolutional Neural Networks for Lung Pattern Analysis
Christodoulidis, S., Anthimopoulos, M., Ebner, L., Christe, A., & Mougiakakou, S. G. (2016) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2636929 · Cited by 303
Prenatal detection of congenital heart disease—results of a national screening programme
van Velzen, C., Clur, S., Rijlaarsdam, M., Bax, C., Pajkrt, E., Heymans, M., Bekker, M., Hruda, J., de Groot, C., Blom, N., et al. (2016) · BJOG: An International Journal of Obstetrics & Gynaecology · doi:10.1111/1471-0528.13274 · Cited by 240
Chest x-ray generation and data augmentation for cardiovascular abnormality classification
Moradi, M., Madani, A., Karargyris, A., & Syeda-Mahmood, T. F. (2018) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.2293971 · Cited by 172
Racial differences in infant mortality attributable to birth defects in the United States, 1989–2002
Yang, Q., Chen, H., Correa, A., Devine, O. J., Mathews, T. J., & Honein, M. A. (2006) · Birth Defects Research Part A Clinical and Molecular Teratology · doi:10.1002/bdra.20308 · Cited by 146
Abdi, A. H., Luong, C. L., Tsang, T. S. M., Allan, G., Nouranian, S., Jue, J., Hawley, D., Fleming, S., Gin, K., Swift, J. L., et al. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2690836 · Cited by 141
Chaoui, R. (2003) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.187 · Cited by 122
Recognizing End-Diastole and End-Systole Frames via Deep Temporal Regression Network
Kong, B., Zhan, Y., Shin, M. C., Denny, T., & Zhang, S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46726-9_31 · Cited by 117
ONE-CLASS DETECTION OF CELL STATES IN TUMOR SUBTYPES
Sokolov, A., Paull, E., & Stuart, J. M. (2015) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789814749411_0037 · Cited by 94
A DEEP CONVOLUTIONAL AUTO-ENCODER WITH POOLING – UNPOOLING LAYERS IN CAFFE
Turchenko, V., Chalmers, E., & Luczak, A. (2019) · International Journal of Computing · doi:10.47839/ijc.18.1.1270 · Cited by 93
Medical Image Synthesis with Generative Adversarial Networks for Tissue Recognition
Zhang, Q., Wang, H., Lu, H., Won, D., & Yoon, S. W. (2018) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi.2018.00030 · Cited by 80
Describing ultrasound video content using deep convolutional neural networks
Gao, Y., Maraci, M. A., & Noble, J. A. (2016) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2016.7493384 · Cited by 46
A systematic review of prenatal screening for congenital heart disease by fetal electrocardiography
Verdurmen, K. M. J., Eijsvoogel, N. B., Lempersz, C., Vullings, R., Schroer, C., van Laar, J. O. E. H., & Oei, S. G. (2016) · International Journal of Gynecology & Obstetrics · doi:10.1016/j.ijgo.2016.05.010 · Cited by 33
Screening of fetal congenital heart disease: the challenge continues
Rocha, L. A., Júnior, E. A., Nardozza, L. M. M., & Moron, A. F. (2013) · Brazilian Journal of Cardiovascular Surgery · doi:10.5935/1678-9741.20130048 · Cited by 21
Lansey, E., Pishbin, N., Gollub, J. N., & Crouse, D. T. (2012) · Journal of the Optical Society of America B · doi:10.1364/josab.29.000262 · Cited by 7
van Velzen, C. L., Pajkrt, E., & Haak, M. C. (2015) · BJOG An International Journal of Obstetrics & Gynaecology · doi:10.1111/1471-0528.13417 · Cited by 6
Screening of fetal congenital heart disease: the challenge continues.
Ithuralde, M., & Neirotti, R. A. (2014) · PubMed · Cited by 2
CHAUBAL, N. G. (2017) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2017.08.1376 · Cited by 1
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Garcea, F., Serra, A., Lamberti, F., & Morra, L. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106391 · Cited by 451
A review on deep-learning algorithms for fetal ultrasound-image analysis
Fiorentino, M. C., Villani, F. P., Cosmo, M. D., Frontoni, E., & Moccia, S. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102629 · Cited by 224
Atasever, S., Azgınoğlu, N., Terzi, D. S., & Terzi, R. (2022) · Clinical Imaging · doi:10.1016/j.clinimag.2022.11.003 · Cited by 134
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Ramirez Zegarra, R., & Ghi, T. (2023) · Ultrasound in Obstetrics & Gynecology · doi:10.1002/uog.26130 · Cited by 102
Zhang, Y., Wang, J., Zhao, J., Huang, G., Liu, K., Pan, W. D., Sun, L., Li, J., Xu, W., He, C., et al. (2023) · The Lancet Child & Adolescent Health · doi:10.1016/s2352-4642(23)00051-2 · Cited by 88
Self-citation
Metadata
- Authors
- Gong, Y., Zhang, Y., Zhu, H., Lv, J., Cheng, Q., Zhang, H., He, Y., & Wang, S.
- Affiliations
- State Key Laboratory of Software Development Environment (CN)
- Capital Medical University (CN)
- Beijing Institute of Technology (CN)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2020-04
- Volume
- 39
- Issue
- 4
- Pages
- 1206-1222
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2979616729
- Cited by
- 101
- References
- 45
- Citations by year
- 2026: 17 · 2025: 18 · 2024: 20 · 2023: 14 · 2022: 18 · 2021: 12 · 2020: 2
- FWCI
- 6.09
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- Funders
- State Key Laboratory of Software Development Environment
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, China
- Beihang University
- National Natural Science Foundation of China (61702027)
- Beijing Science and Technology Planning Project (Z171100000117022, Z181100001918008)
- Beijing Municipal Science and Technology Commission (PXM2018014226000013)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Congenital Heart Disease Studies
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- fetal congenital heart disease, fetal echocardiography, automated screening, one-class classification, adversarial learning, WGAN-GP, transfer learning, data augmentation, limited labeled data
- MeSH terms
- Deep Learning, Algorithms, Echocardiography, Female, Heart Defects, Congenital, Humans, Image Interpretation, Computer-Assisted, Pregnancy, Video Recording, Ultrasonography, Prenatal
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