FLDS: An Intelligent Feature Learning Detection System for Visualizing Medical Images Supporting Fetal Four-Chamber Views

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 · see citing works

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 56,035 · see citing works

You Only Look Once: Unified, Real-Time Object Detection

Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803 · see citing works

Squeeze-and-Excitation Networks

Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667 · see citing works

Feature Pyramid Networks for Object Detection

Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939 · see citing works

Fast R-CNN

Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105 · see citing works

CBAM: Convolutional Block Attention Module

Woo, S., Park, J., Lee, J.-Y., & Kweon, I. S. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_1 · Cited by 25,853 · see citing works

Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.74 · Cited by 24,513 · see citing works

Learning Multiple Layers of Features from Tiny Images

Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,890 · see citing works

YOLO9000: Better, Faster, Stronger

Redmon, J., & Farhadi, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.690 · Cited by 19,892 · see citing works

YOLOv3: An Incremental Improvement

Redmon, J., & Farhadi, A. (2018) · arXiv · doi:10.48550/arxiv.1804.02767 · Cited by 17,141 · see citing works

A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper)

Khanam, Z., Achari, V. P. S., Boukhennoufa, I., et al. (2024) · Durham Research Online (Durham University) · doi:10.4230/oasics.ng-res.2024.2 · Cited by 14,048 · see citing works

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2389824 · Cited by 11,971 · see citing works

A model of saliency-based visual attention for rapid scene analysis

Itti, L., Christof Koch, A., & Niebur, E. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.730558 · Cited by 11,289 · see citing works

YOLOv4: Optimal Speed and Accuracy of Object Detection

Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y. M. (2020) · arXiv · doi:10.48550/arxiv.2004.10934 · Cited by 10,307 · see citing works

Path Aggregation Network for Instance Segmentation

Liu, S., Qi, L., Qin, H., Shi, J., & Jia, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00913 · Cited by 10,011 · see citing works

The Pascal Visual Object Classes Challenge: A Retrospective

Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-014-0733-5 · Cited by 7,139 · see citing works

SGDR: Stochastic Gradient Descent with Warm Restarts

Loshchilov, I., & Hutter, F. (2016) · arXiv · doi:10.48550/arxiv.1608.03983 · Cited by 5,309 · see citing works

Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression

Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., & Ren, D. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6999 · Cited by 5,288 · see citing works

Residual Attention Network for Image Classification

Wang, F., Jiang, M., Qian, C., Yang, S., Li, C., Zhang, H., Wang, X., & Tang, X. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.683 · Cited by 4,073 · see citing works

Deep Learning for Segmentation Using an Open Large-Scale Dataset in 2D Echocardiography

Leclerc, S., Smistad, E., Pedrosa, J., Ostvik, A., Cervenansky, F., Espinosa, F., Espeland, T., Berg, E. A. R., Jodoin, P., Grenier, T., et al. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2900516 · Cited by 841 · see citing works

Deep Learning in Medical Ultrasound Analysis: A Review

Liu, S., Wang, Y., Yang, X., Lei, B., Liu, L., Li, S. X., Ni, D., & Wang, T. (2019) · Engineering · doi:10.1016/j.eng.2018.11.020 · Cited by 798 · see citing works

Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans

Cheng, J.-Z., Ni, D., Chou, Y.-H., Qin, J., Tiu, C.-M., Chang, Y.-C., Huang, C.-S., Shen, D., & Chen, C.-M. (2016) · Scientific Reports · doi:10.1038/srep24454 · Cited by 755 · see citing works

Anatomically constrained neural networks (ACNNS): application to cardiac image enhancement and segmentation

Oktay, O., Ferrante, E., Kamnitsas, K., Heinrich, M., Bai, W., Caballero, J., Cook, S. A., de Marvao, A., Dawes, T., O'Regan, D. P., et al. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2743464 · Cited by 751 · cited 2× · see citing works

Few-Shot Object Detection With Attention-RPN and Multi-Relation Detector

Fan, Q., Zhuo, W., Tang, C., & Tai, Y. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00407 · Cited by 680 · see citing works

Learning to combine foveal glimpses with a third-order Boltzmann machine

Larochelle, H., & Hinton, G. E. (2010) · Cited by 433 · see citing works

SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound

Baumgartner, C. F., Kamnitsas, K., Matthew, J., Fletcher, T. P., Smith, S., Koch, L. M., Kainz, B., & Rueckert, D. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2712367 · Cited by 416 · see citing works

Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks

Chen, H., Ni, D., Qin, J., Li, S., Yang, X., Wang, T., & Heng, P. A. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2425041 · Cited by 379 · see citing works

Prime Sample Attention in Object Detection

Cao, Y., Chen, K., Loy, C. C., & Lin, D. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01160 · Cited by 258 · see citing works

EEG signal classification using universum support vector machine

Richhariya, B., & Tanveer, M. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.03.053 · Cited by 256 · see citing works

Stacked deep polynomial network based representation learning for tumor classification with small ultrasound image dataset

Shi, J., Zhou, S., Liu, X., Zhang, Q., Lu, M., & Wang, T. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.01.074 · Cited by 166 · see citing works

Focusnet: An Attention-Based Fully Convolutional Network for Medical Image Segmentation

Kaul, C., Manandhar, S., & Pears, N. E. (2019) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2019.8759477 · Cited by 156 · see citing works

Prenatal Imaging

Reddy, U. M., Filly, R. A., & Copel, J. A. (2008) · Obstetrics and Gynecology · doi:10.1097/01.aog.0000318871.95090.d9 · Cited by 148 · see citing works

A reduced universum twin support vector machine for class imbalance learning

Richhariya, B., & Tanveer, M. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2019.107150 · Cited by 124 · see citing works

A Generic Quality Control Framework for Fetal Ultrasound Cardiac Four-Chamber Planes

Dong, J., Liu, S., Liao, Y., Wen, H., Lei, B., Li, S., & Wang, T. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2948316 · Cited by 104 · see citing works

Fetal Congenital Heart Disease Echocardiogram Screening Based on DGACNN: Adversarial One-Class Classification Combined with Video Transfer Learning

Gong, Y., Zhang, Y., Zhu, H., Lv, J., Cheng, Q., Zhang, H., He, Y., & Wang, S. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2946059 · Cited by 101 · see citing works

Robust energy-based least squares twin support vector machines

Tanveer, M., Khan, M. A., & Ho, S.-S. (2016) · Applied Intelligence · doi:10.1007/s10489-015-0751-1 · Cited by 99 · see citing works

DW-Net: A cascaded convolutional neural network for apical four-chamber view segmentation in fetal echocardiography

Xu, L., Liu, M., Shen, Z., Wang, H., Liu, X., Wang, X., Wang, S., Li, T., Yu, S., Hou, M., et al. (2020) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2019.101690 · Cited by 87 · see citing works

A robust fuzzy least squares twin support vector machine for class imbalance learning

Richhariya, B., & Tanveer, M. (2018) · Applied Soft Computing · doi:10.1016/j.asoc.2018.07.003 · Cited by 78 · see citing works

Time-Variant Graph Classification

Wang, H., Wu, J. X., Xingquan Zhu, A., Chen, Y., & Zhang, C. (2018) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2018.2830792 · Cited by 66 · see citing works

A framework for analysis of linear ultrasound videos to detect fetal presentation and heartbeat

Maraci, M. A., Bridge, C. P., Napolitano, R., Papageorghiou, A. T., & Noble, J. A. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.01.003 · Cited by 65 · see citing works

Automated annotation and quantitative description of ultrasound videos of the fetal heart

Bridge, C. P., Ioannou, C., & Noble, J. A. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.11.006 · Cited by 64 · see citing works

Automated characterization of the fetal heart in ultrasound images using fully convolutional neural networks

Sundaresan, V., Bridge, C. P., Ioannou, C., & Noble, J. A. (2017) · 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) · doi:10.1109/isbi.2017.7950609 · Cited by 58 · see citing works

Autism Screening Using Deep Embedding Representation

Wang, H., Li, L., Chi, L., & Zhao, Z. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-22741-8_12 · Cited by 33 · see citing works

Least squares projection twin support vector clustering (LSPTSVC)

Richhariya, B., & Tanveer, M. (2020) · Information Sciences · doi:10.1016/j.ins.2020.05.001 · Cited by 26 · see citing works

Pinball Loss Twin Support Vector Clustering

Tanveer, M., Gupta, T., Shah, M., & Initiative, A. D. N. (2021) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3409264 · Cited by 25 · see citing works

Automated embolic signal detection using Deep Convolutional Neural Network

Sombune, P., Phienphanich, P., Phuechpanpaisal, S., Muengtaweepongsa, S., Ruamthanthong, A., & Tantibundhit, C. (2017) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2017.8037577 · Cited by 23 · see citing works

Regularized robust fuzzy least squares twin support vector machine for class imbalance learning

Ganaie, M. A., Tanveer, M., & Suganthan, P. N. (2020) · doi:10.1109/ijcnn48605.2020.9207724 · Cited by 22 · see citing works

Automated detection of masses on whole breast volume ultrasound scanner: false positive reduction using deep convolutional neural network

Hiramatsu, Y., Muramatsu, C., Kobayashi, H., Hara, T., & Fujita, H. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2254581 · Cited by 21 · see citing works

Improved universum twin support vector machine

Richhariya, B., Sharma, A. K., & Tanveer, M. (2018) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2018.8628671 · Cited by 20 · see citing works

Sparse Twin Support Vector Clustering Using Pinball Loss

Tanveer, M., Gupta, T., Shah, M., & Richhariya, B. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3059910 · Cited by 20 · see citing works

Multi-anatomy localization in fetal echocardiography videos

Patra, A., & Noble, J. A. (2019) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2019.8759551 · Cited by 18 · see citing works

Universum least squares twin parametric-margin support vector machine

Richhariya, B., & Tanveer, M. (2020) · doi:10.1109/ijcnn48605.2020.9206865 · Cited by 10 · see citing works

Artificial intelligence in obstetric ultrasound: A scoping review.

Horgan, R., Nehme, L., & Abuhamad, A. (2023) · Prenatal Diagnosis · doi:10.1002/pd.6411 · Cited by 72 · see citing works

Enhancing fetal electrocardiogram classification: A hybrid approach incorporating multimodal data fusion and advanced deep learning models

Ziani, S. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17305-6 · Cited by 47 · see citing works

Advances in the Application of Artificial Intelligence in Fetal Echocardiography

Zhang, J., Xiao, S., Zhu, Y. C., Zhang, Z., Cao, H., Xie, M., & Zhang, L. (2024) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2023.12.013 · Cited by 61 · see citing works

Wearable Sensors for Vital Signs Measurement: A Survey

Lv, Z., & Li, Y. (2022) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan11010019 · Cited by 43 · see citing works

RLDS: An explainable residual learning diagnosis system for fetal congenital heart disease

Qiao, S., Pang, S., Luo, G., Pan, S., Yu, Z., Chen, T., & Lv, Z. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2021.10.001 · Cited by 42 · see citing works

TransFusionNet: Semantic and Spatial Features Fusion Framework for Liver Tumor and Vessel Segmentation Under JetsonTX2

Wang, X., Zhang, X., Wang, G., Zhang, Y., Shi, X., Dai, H., Liu, M., Wang, Z., & Meng, X. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3207233 · Cited by 34 · see citing works

A Pseudo-Siamese Feature Fusion Generative Adversarial Network for Synthesizing High-Quality Fetal Four-Chamber Views

Qiao, S., Pan, S., Luo, G., Pang, S., Chen, T., Singh, A. K., & Lv, Z. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3143319 · Cited by 32 · see citing works

SPReCHD: Four-Chamber Semantic Parsing Network for Recognizing Fetal Congenital Heart Disease in Medical Metaverse

Qiao, S., Pang, S., Sun, Y., Luo, G., Yin, W., Zhao, Y., Pan, S., & Lv, Z. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3218577 · Cited by 28 · see citing works

DPC-MSGATNet: dual-path chain multi-scale gated axial-transformer network for four-chamber view segmentation in fetal echocardiography

Qiao, S.-B., Pang, S.-C., Luo, G., Sun, Y., Yin, W.-J., Pan, S.-L., & Lv, Z. (2023) · Complex & Intelligent Systems · doi:10.1007/s40747-023-00968-x · Cited by 21 · see citing works

Role of Four-Chamber Heart Ultrasound Images in Automatic Assessment of Fetal Heart: A Systematic Understanding

Gudigar, A., Raghavendra, U., Samanth, J., Vasudeva, A., Ashwal, A. J., Nayak, K., Tan, R., Ciaccio, E. J., Ooi, C. P., Barua, P. D., et al. (2022) · Informatics · doi:10.3390/informatics9020034 · Cited by 15 · see citing works

Diagnostic accuracy of artificial intelligence models in detecting congenital heart disease in the second-trimester fetus through prenatal cardiac screening: a systematic review and meta-analysis

Liastuti, L. D., & Nursakina, Y. (2025) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2025.1473544 · Cited by 15 · see citing works

A comprehensive scoping review on machine learning-based fetal echocardiography analysis

Hernandez-Cruz, N., Patey, O. V., Teng, C., Papageorghiou, A. T., & Noble, J. A. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.109666 · Cited by 12 · see citing works

TVS: a trusted verification scheme for office documents based on blockchain

Zhai, X. M., Pang, S., Wang, M., Qiao, S., & Lv, Z. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00617-1 · Cited by 10 · see citing works

The Role of Ensemble Learning in Advancing Child Fetal Health: A Focus on Voting and Bagging Techniques

Swain, S., Patra, K. J., Jayasingh, S. K., Mohanty, M. N., & Pattanayak, B. K. (2024) · International Conference on Cognitive, Green and Ubiquitous Computing (IC-CGU) · doi:10.1109/ic-cgu58078.2024.10530780 · Cited by 10 · see citing works

An algorithm for cardiac disease detection based on the magnetic resonance imaging

Li, H., Yuan, Q., Wang, Y., Qu, P., Jiang, C., & Kuang, H. (2025) · Scientific Reports · doi:10.1038/s41598-025-88567-3 · Cited by 7 · see citing works

TKR-FSOD: Fetal Anatomical Structure Few-Shot Detection Utilizing Topological Knowledge Reasoning

Li, X., Tan, Y., Liang, B., Pu, B., Yang, J., Zhao, L., Kong, Y., Yang, L., Zhang, R., Li, H., et al. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3480197 · Cited by 7 · see citing works

Computer-aided assessment for enlarged fetal heart with deep learning model

Nurmaini, S., Sapitri, A. I., Roseno, M. T., Rachmatullah, M. N., Mirani, P., Bernolian, N., Darmawahyuni, A., Tutuko, B., Firdaus, F., Islami, A., et al. (2025) · iScience · doi:10.1016/j.isci.2025.112288 · Cited by 6 · see citing works

Segmentation of ten fetal heart components with coarse‐to‐fine cascading and dynamic feature powering

Yang, T., Zhang, Y., Zhu, M., Wang, Y., An, S., Gu, X., Liu, X., Han, J., He, Y., & Zhu, H. (2022) · IET Image Processing · doi:10.1049/ipr2.12597 · Cited by 6 · see citing works

Segmentation of four-chamber view images in fetal ultrasound exams using a novel deep learning model ensemble method

Hurtado, J., Sierra-Franco, C. A., Motta, T., & Raposo, A. B. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109188 · Cited by 5 · see citing works

A Vision sensing-based automatic evaluation method for teaching effect based on deep residual network

Sun, M. (2023) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2023275 · Cited by 4 · see citing works

4D foetal cardiac ultrasound image detection based on deep learning with weakly supervised localisation for rapid diagnosis of evolving hypoplastic left heart syndrome

Wang, G., Li, W., Zhou, M., Zhu, H., Yang, G., & Yap, C. H. (2024) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.12354 · Cited by 4 · see citing works

A deep learning-based intelligent analysis platform for fetal ultrasound four-chamber views

Qiao, S., Pang, S., Dong, Y., Gui, H., Yuan, Q., Zheng, Z., & Cui, G. (2022) · International Conference on Information Science, Parallel and Distributed Systems (ISPDS) · doi:10.1109/ispds56360.2022.9874029 · Cited by 4 · see citing works

Investigation on ultrasound images for detection of fetal congenital heart defects

Satish, S., & Rufus, N. H. A. (2024) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/ad4f91 · Cited by 4 · see citing works

Guest Editorial Advanced Machine Learning Algorithms for Biomedical Data and Imaging

Tanveer, M., Lin, C., & Singh, A. K. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3204385 · Cited by 3 · see citing works

Deep Learning-Driven Innovations in Echocardiography: Taxonomy, Clinical Impact, Challenges, and Opportunities

Monkam, P., Wang, X., Liu, S., Qian, W., Yao, Y., Shi, X., & Qi, S. (2025) · Annals of Biomedical Engineering · doi:10.1007/s10439-025-03944-3 · Cited by 2 · see citing works

Implementation of resource-efficient fetal echocardiography detection algorithms in edge computing

Zhu, Y., Gao, Y. D., Wang, M., Li, M., & Wang, K. (2024) · PLoS ONE · doi:10.1371/journal.pone.0305250 · Cited by 2 · see citing works

An efficient network with state space model under evidential training for fetal echocardiography standard view recognition

Chen, C., Liu, Y., Liang, T., Lin, S., Han, X., Liu, X., Yang, J. M., Zhang, Y., & Yan, X. (2025) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-025-03347-5 · Cited by 2 · see citing works

Fetal ultrasound four-chamber view editing synthesis via denoising diffusion model

Qiao, S., Huang, M., Luo, G., Yin, W., Li, H., Wang, M., & Lv, Z. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128939 · Cited by 1 · see citing works

Complementary role of echocardiography, karyotyping, and chromosomal microarray in congenital cardiac anomalies

Yin, J., Zhang, X., Wang, Q., Cao, Q., Ou, H., Zhao, Z., Xu, S., Wang, J., Li, X., Zhang, B., et al. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1586161 · Cited by 1 · see citing works

Enhancing Fetal Cardiac Imaging With Artificial Intelligence

Martins, J. G., Horgan, R., & Sinkovskaya, E. S. (2025) · Clinical Obstetrics & Gynecology · doi:10.1097/grf.0000000000000992 · Cited by 1 · see citing works

Automated interpretation of fetal cardiac function evaluation from the echocardiogram

Huang, C., Zhang, L., Xie, B., Jiang, Y., Zhu, Y., Liu, X., Lei, T., He, M., Yan, Y., Wang, N., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02381-3 · Cited by 1 · see citing works

A universal lesion detection method based on partially supervised learning

Wang, X., Shi, X., Meng, X., Zhang, Z., & Zhang, C. (2023) · Frontiers in Pharmacology · doi:10.3389/fphar.2023.1084155

Remote Sensing Target Detection Inspired by Scene Information and Inter-Object Relations

Ding, Y., Lyu, X., Yan, L., Rong, L., & Cheng, K. (2023) · Computing and Informatics · doi:10.31577/cai_2023_6_1404

Sağlık Hizmetlerinde Yapay Zeka: Temel Kavramlar ve Sınıflandırmalar

Yönden, H. (2025) · Akademisyen Kitabevi eBooks · doi:10.37609/akya.3461

Recognition of Normal Fetal Echocardiograms Based on an Explainable Denoising Deep Learning Model

Song, S., Liu, Y., Xu, G., Yang, Y., Ouyang, M., & Zeng, S. (2026) · Journal of Clinical Ultrasound · doi:10.1002/jcu.70183

Diff-MOCS: A Diffusion Model-Based with Multi-Object Cardiac Ultrasound Images Segmentation

Ye, S., Zhang, Y., Meng, X., Li, C., & Sun, W. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356278

Artificial Intelligence in Feto-maternal Health: A Systematic Review of Predictive Models, Validation, and Clinical TranslationPreprint

Awe, O. D., Oliveira, A. P. D. M. E., Charles, C. M., Oladimeji, K. E., Torezzan, C., & Pacagnella, R. C. (2026) · Research Square · doi:10.21203/rs.3.rs-8651301/v1 · Cited by 1 · see citing works

Cross-Center Online Generalization Algorithm with Unadversarial Consistency for Fetal Heart Ultrasound View Recognition

Liu, Y., Liang, T., Tian, J., Jiang, N., Chen, C. K., Zhang, Y., & Zhang, Z. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-026-02100-0

Three-phase learning framework for first-trimester fetal echocardiography plan classification via knowledge distillation and CBAM-enhanced DenseNet

Quyen, H. M., Thuy, B. H. N., Pham, P., Tung, N. T., & Vo, B. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110959

Fetal Ultrasound Plane Classification with FetalCLIP Embeddings and Linear Classifiers

Ćaleta, K., Brkić, A., Veček, N., Vranić, M., & Božek, J. (2026) · International Symposium ELMAR (ELMAR) · doi:10.1109/elmar71231.2026.11712193

First‐trimester Cardiac Assessment in Fetuses With Increased Nuchal Translucency: A Diagnostic Accuracy Study

Yu, J., & Gu, Y. (2026) · Echocardiography · doi:10.1111/echo.70650

Automatic Detection of Cardiac Chambers Using an Attention-based YOLOv4 Framework from Four-chamber View of Fetal EchocardiographyPreprint

Qiao, S., Pang, S., Luo, G., Pan, S., Wang, X., Wang, M., Zhai, X., & Chen, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.13096 · Cited by 6 · see citing works

A deep learning-based intelligent analysis platform for fetal ultrasound four-chamber views

Qiao, S., Pang, S., Dong, Y., Gui, H., Yuan, Q., Zheng, Z., & Cui, G. (2022) · International Conference on Information Science, Parallel and Distributed Systems (ISPDS) · doi:10.1109/ispds56360.2022.9874029 · Cited by 4 · see citing works

Congenital heart disease (CHD) discrimination in fetal echocardiogram based on 3D feature fusion

Ji, L., Gu, Y., Sun, K., Yang, J., & Qiao, Y. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7532994 · Cited by 6 · see citing works

A Pseudo-Siamese Feature Fusion Generative Adversarial Network for Synthesizing High-Quality Fetal Four-Chamber Views

Qiao, S., Pan, S., Luo, G., Pang, S., Chen, T., Singh, A. K., & Lv, Z. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3143319 · Cited by 32 · see citing works

SPReCHD: Four-Chamber Semantic Parsing Network for Recognizing Fetal Congenital Heart Disease in Medical Metaverse

Qiao, S., Pang, S., Sun, Y., Luo, G., Yin, W., Zhao, Y., Pan, S., & Lv, Z. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3218577 · Cited by 28 · see citing works