Detecting Anatomical Landmarks From Limited Medical Imaging Data Using Two-Stage Task-Oriented Deep Neural Networks

Random Forests

Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works

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 · see citing works

Histograms of Oriented Gradients for Human Detection

Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584 · see citing works

TensorFlow: A system for large-scale machine learningPreprint

Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.5555/3026877.3026899 · Cited by 16,369 · see citing works

R-FCN: Object Detection via Region-based Fully Convolutional NetworksPreprint

Dai, J., Li, Y., He, K., & Sun, J. Z. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.06409 · Cited by 5,135 · see citing works

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., & LeCun, Y. (2013) · arXiv · doi:10.48550/arxiv.1312.6229 · Cited by 2,680 · see citing works

Facial Landmark Detection by Deep Multi-task Learning

Zhang, Z., Luo, P., Loy, C. C., & Tang, X. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10599-4_7 · Cited by 1,410 · see citing works

Deep Convolutional Network Cascade for Facial Point Detection

Sun, Y. J., Wang, X., & Tang, X. (2013) · 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/cvpr.2013.446 · Cited by 1,405 · see citing works

Efficient object localization using Convolutional Networks

Tompson, J., Goroshin, R., Jain, A., LeCun, Y., & Bregler, C. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298664 · Cited by 1,365 · see citing works

Multi-atlas segmentation of biomedical images: A survey

Iglesias, J. E., & Sabuncu, M. R. (2015) · Medical Image Analysis · doi:10.1016/j.media.2015.06.012 · Cited by 667 · see citing works

From Facial Parts Responses to Face Detection: A Deep Learning Approach

Yang, S., Luo, P., Loy, C. C., & Tang, X. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.419 · Cited by 574 · see citing works

Robust and Accurate Shape Model Fitting Using Random Forest Regression Voting

Cootes, T. F., Ionita, M. C., Lindner, C., & Sauer, P. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33786-4_21 · Cited by 304 · see citing works

Regressing Heatmaps for Multiple Landmark Localization Using CNNs

Payer, C., Štern, D., Bischof, H., & Urschler, M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46723-8_27 · Cited by 248 · see citing works

Detecting Anatomical Landmarks for Fast Alzheimer’s Disease Diagnosis

Zhang, J., Gao, Y., Gao, Y.-Z., Munsell, B., & Shen, D. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2582386 · Cited by 214 · see citing works

A novel matrix-similarity based loss function for joint regression and classification in ad diagnosis

Zhu, X., Suk, H.-I., & Shen, D. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.05.078 · Cited by 202 · see citing works

Fully Automatic Segmentation of the Proximal Femur Using Random Forest Regression Voting

Lindner, C., Thiagarajah, S., Wilkinson, J. M., The Consortium, Wallis, G. A., & Cootes, T. F. (2013) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2013.2258030 · Cited by 198 · see citing works

Alzheimer's Disease Diagnosis Using Landmark-Based Features From Longitudinal Structural MR Images

Zhang, J., Liu, M., An, L., Gao, Y., & Shen, D. G. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2704614 · Cited by 181 · see citing works

An Artificial Agent for Anatomical Landmark Detection in Medical Images

Ghesu, F. C., Georgescu, B., Mansi, T., Neumann, D., Joachim Hornegger, A., & Comaniciu, D. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46726-9_27 · Cited by 160 · see citing works

View-aligned hypergraph learning for Alzheimer’s disease diagnosis with incomplete multi-modality data

Liu, M., Zhang, J., Yap, P., & Shen, D. G. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.11.002 · Cited by 159 · see citing works

Local Energy Pattern for Texture Classification Using Self-Adaptive Quantization Thresholds

Zhang, J., Liang, J., & Zhao, H. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2214045 · Cited by 151 · see citing works

3D Deep Learning for Efficient and Robust Landmark Detection in Volumetric Data

Zheng, Y., Liu, D., Georgescu, B., Nguyen, H. V., & Comaniciu, D. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24553-9_69 · Cited by 151 · see citing works

Decision Forests with Long-Range Spatial Context for Organ Localization in CT Volumes

Criminisi, A., & Bucciarelli, S. (2009) · Cited by 140 · see citing works

Joint Binary Classifier Learning for ECOC-Based Multi-Class Classification

Liu, M., Zhang, D., Chen, S., & Xue, H. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2430325 · Cited by 91 · see citing works

Cascaded Collaborative Regression for Robust Facial Landmark Detection Trained Using a Mixture of Synthetic and Real Images With Dynamic Weighting

Feng, Z., Hu, G., Kittler, J., Christmas, W. J., & Wu, X. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2446944 · Cited by 89 · see citing works

Automatic localization of the left ventricle in cardiac MRI images using deep learning

Emad, O., Yassine, I. A., & Fahmy, A. S. (2015) · doi:10.1109/embc.2015.7318454 · Cited by 80 · see citing works

Stratified Decision Forests for Accurate Anatomical Landmark Localization in Cardiac Images

Oktay, O., Bai, W., Guerrero, R., Rajchl, M., de Marvao, A., O’Regan, D. P., Cook, S. A., Heinrich‬, M. P., Glocker, B., & Rueckert, D. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2597270 · Cited by 74 · see citing works

An evidential classifier based on feature selection and two-step classification strategy

Lian, C., Ruan, S., & Denœux, T. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.01.019 · Cited by 65 · see citing works

Deep learning for automatic localization, identification, and segmentation of vertebral bodies in volumetric MR images

Suzani, A., Rasoulian, A., Seitel, A., Fels, S., Rohling, R. N., & Abolmaesumi, P. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2081542 · Cited by 64 · see citing works

Continuous rotation invariant local descriptors for texton dictionary-based texture classification

Zhang, J., Zhao, H., & Liang, J. (2012) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2012.10.004 · Cited by 64 · see citing works

Automatic X-ray landmark detection and shape segmentation via data-driven joint estimation of image displacements

Chen, C., Xie, W., Franke, J., Grützner, P.-A., Nolte, L.-P., & Guoyan Zheng, A. (2014) · Medical Image Analysis · doi:10.1016/j.media.2014.01.002 · Cited by 61 · see citing works

Automatic Craniomaxillofacial Landmark Digitization via Segmentation-guided Partially-joint Regression Forest Model and Multi-scale Statistical Features

Zhang, J., Gao, Y.-Z., Wang, L., Tang, Z., Xia, J., & Shen, D. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2503421 · Cited by 54 · see citing works

Robust Automatic Knee MR Slice Positioning Through Redundant and Hierarchical Anatomy Detection

Zhan, Y., Dewan, M., Harder, M., Krishnan, A., & Zhou, X. (2011) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2011.2162634 · Cited by 50 · see citing works

Dual Sparse Constrained Cascade Regression for Robust Face Alignment

Liu, Q., Deng, J., & Tao, D. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2502485 · Cited by 37 · see citing works

Automatic Labeling of Anatomical Structures in MR FastView Images Using a Statistical Atlas

Fenchel, M., Thesen, S., & Schilling, A. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85988-8_69 · Cited by 34 · see citing works

Unconstrained Facial Landmark Localization with Backbone-Branches Fully-Convolutional NetworksPreprint

Liang, Z., Ding, S., & Lin, L. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1507.03409 · Cited by 31 · see citing works

Learning-Based Multimodal Image Registration for Prostate Cancer Radiation Therapy

Cao, X., Gao, Y., Yang, J., Guorong Wu, A., & Shen, D. G. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46726-9_1 · Cited by 28 · see citing works

Automatic spine and pelvis detection in frontal X-rays using deep neural networks for patch displacement learning

Aubert, B., Vázquez, C., Cresson, T., Stefan Parent, D., & de Guise, J. A. (2016) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2016.7493535 · Cited by 24 · see citing works

Generalized sparse MRF appearance models

Donner, R., Langs, G., Mičušík, B., & Bischof, H. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2009.07.010 · Cited by 22 · see citing works

Anatomical Landmark Detection in Medical Applications Driven by Synthetic Data

Riegler, G., Urschler, M., Rüther, M., Bischof, H., & Štern, D. (2015) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2015.21 · Cited by 17 · see citing works

Deep learning in medical imaging and radiation therapy

Sahiner, B., Pezeshk, A. X., Hadjiiski, L. M., Wang, X., Drukker, K., Cha, K. H., Summers, R. M., & Giger, M. L. (2018) · Medical Physics · doi:10.1002/mp.13264 · Cited by 739 · see citing works

Hierarchical fully convolutional network for joint atrophy localization and Alzheimer’s disease diagnosis using structural MRI

Lian, C., Liu, M., Zhang, J., & Shen, D. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2889096 · Cited by 544 · see citing works

Convolutional Neural Networks for Radiologic Images: A Radiologist’s Guide

Soffer, S., Ben-Cohen, A., Shimon, O., Amitai, M. M., Greenspan, H. K., & Klang, E. (2019) · Radiology · doi:10.1148/radiol.2018180547 · Cited by 514 · see citing works

Landmark-based deep multi-instance learning for brain disease diagnosis

Liu, M., Zhang, J., Adeli, E., & Shen, D. G. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.10.005 · Cited by 424 · see citing works

Edge YOLO: Real-Time Intelligent Object Detection System Based on Edge-Cloud Cooperation in Autonomous Vehicles

Liang, S., Wu, H., Zhen, L., Hua, Q., Garg, S., Kaddoum, G., Hassan, M. M., & Yu, K. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3158253 · Cited by 367 · see citing works

Integrating spatial configuration into heatmap regression based CNNs for landmark localization

Payer, C., Štern, D., Bischof, H., & Urschler, M. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.03.007 · Cited by 364 · see citing works

Survey on deep learning for radiotherapy

Meyer, P., Noblet, V., Mazzara, C., & Lallement, A. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.05.018 · Cited by 302 · see citing works

Going Deep in Medical Image Analysis: Concepts, Methods, Challenges, and Future Directions

Altaf, F., Islam, S. M. S., Akhtar, N., & Janjua, N. K. (2019) · IEEE Access · doi:10.1109/access.2019.2929365 · Cited by 293 · see citing works

A brief review on multi-task learning

Thung, K., & Chong-yaw Wee, A. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6463-x · Cited by 266 · see citing works

One-Pass Multi-Task Networks With Cross-Task Guided Attention for Brain Tumor Segmentation

Zhou, C., Ding, C., Wang, X., Lu, Z., & Tao, D. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2973510 · Cited by 249 · see citing works

High-Resolution Encoder–Decoder Networks for Low-Contrast Medical Image Segmentation

Zhou, S., Nie, D., Adeli, E., Yin, J., Lian, J., & Shen, D. G. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2919937 · Cited by 230 · see citing works

Boundary-Weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation

Zhu, Q., Du, B., & Yan, P. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2935018 · Cited by 217 · see citing works

Weakly Supervised Segmentation of COVID19 Infection with Scribble Annotation on CT Images

Liu, X., Yuan, Q., Gao, Y., He, K., Wang, S., Tang, X., Tang, J., & Shen, D. G. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108341 · Cited by 193 · see citing works

A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-19 pneumonia from chest X-ray images

Wang, G., Liu, X., Shen, J., Wang, C., Li, Z., Ye, L., Wu, X., Chen, T., Wang, K., Zhang, X., et al. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-021-00704-1 · Cited by 192 · see citing works

Evaluating reinforcement learning agents for anatomical landmark detection

Alansary, A., Oktay, O., Li, Y., Folgoc, L. L., Hou, B., Vaillant, G., Kamnitsas, K., Vlontzos, A., Glocker, B., Kainz, B., et al. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.02.007 · Cited by 185 · see citing works

Hierarchical Convolutional Neural Networks for Segmentation of Breast Tumors in MRI With Application to Radiogenomics

Zhang, J., Saha, A., Zhu, Z., & Mazurowski, M. A. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2865671 · Cited by 175 · see citing works

Hippocampus Analysis by Combination of 3-D DenseNet and Shapes for Alzheimer's Disease Diagnosis

Cui, R., & Liu, M. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2882392 · Cited by 164 · see citing works

Anatomical Landmark Based Deep Feature Representation for MR Images in Brain Disease Diagnosis

Liu, M., Zhang, J., Nie, D., Yap, P., & Shen, D. G. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2791863 · Cited by 160 · see citing works

Multiscale lightweight 3D segmentation algorithm with attention mechanism: Brain tumor image segmentation

Liu, H., Huo, G., Li, Q., Guan, X., & Tseng, M. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119166 · Cited by 150 · see citing works

Deep Geodesic Learning for Segmentation and Anatomical Landmarking

Torosdagli, N., Liberton, D. K., Verma, P., Sincan, M., Lee, J. S., & Bağcı, U. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2875814 · Cited by 143 · see citing works

YOLO Object Detection for Real-Time Fabric Defect Inspection in the Textile Industry: A Review of YOLOv1 to YOLOv11

Mao, M., & Hong, M. (2025) · Sensors · doi:10.3390/s25072270 · Cited by 129 · see citing works

Multi-channel multi-scale fully convolutional network for 3D perivascular spaces segmentation in 7T MR images

Lian, C., Zhang, J., Liu, M., Zong, X., Hung, S., Lin, W., & Shen, D. G. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.02.009 · Cited by 127 · see citing works

Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans

Wu, T., Lian, C., Lee, S., Pastewait, M., Piers, C., Liu, J., Wang, F., Wang, L., Chiu, C.-Y., Wang, W., et al. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3180343 · Cited by 126 · see citing works

Deep Learning for Fast and Spatially Constrained Tissue Quantification From Highly Accelerated Data in Magnetic Resonance Fingerprinting

Fang, Z., Chen, Y., Liu, M., Xiang, L., Zhang, Q., Wang, Q., Lin, W., & Shen, D. G. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2899328 · Cited by 125 · see citing works

Deep Learning-Based Regression and Classification for Automatic Landmark Localization in Medical Images

Noothout, J. M. H., de Vos, B. D., Wolterink, J. M., Postma, E. M., Smeets, P. A. M., Takx, R. A. P., Leiner, T., Viergever, M. A., & Išgum, I. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3009002 · Cited by 124 · see citing works

Deformable Image Registration Using a Cue-Aware Deep Regression Network

Cao, X., Yang, J., Zhang, J., Wang, Q., Yap, P., & Shen, D. G. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2822826 · Cited by 123 · see citing works

Inverse-Consistent Deep Networks for Unsupervised Deformable Image RegistrationPreprint

Zhang, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.03443 · Cited by 122 · see citing works

Multi-Scale Context-Guided Deep Network for Automated Lesion Segmentation With Endoscopy Images of Gastrointestinal Tract

Wang, S., Cong, Y., Zhu, H., Chen, X., Qu, L., Fan, H., Zhang, Q., & Liu, M. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2997760 · Cited by 121 · see citing works

Context-guided fully convolutional networks for joint craniomaxillofacial bone segmentation and landmark digitization

Zhang, J., Liu, M., Wang, L., Chen, S., Yuan, P., Li, J., Shen, S. G.-F., Tang, Z., Chen, K.-C., Xia, J. J., et al. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101621 · Cited by 119 · see citing works

Cascaded convolutional networks for automatic cephalometric landmark detection

Zeng, M., Yan, Z., Liu, S., Zhou, Y., & Qiu, L. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101904 · Cited by 118 · see citing works

Deep Anatomical Context Feature Learning for Cephalometric Landmark Detection

Oh, K., Oh, I.-S., Le, V. N. T., & Lee, D. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3002582 · Cited by 105 · see citing works

A holistic overview of deep learning approach in medical imaging

Yousef, R., Gupta, G., Yousef, N., & Khari, M. (2022) · Multimedia Systems · doi:10.1007/s00530-021-00884-5 · Cited by 105 · see citing works

Segmentation of blood vessels using rule-based and machine-learning-based methods: a review

Zhao, F., Chen, Y., Hou, Y., & He, X. (2017) · Multimedia Systems · doi:10.1007/s00530-017-0580-7 · Cited by 82 · see citing works

Multi-task neural networks for joint hippocampus segmentation and clinical score regression

Cao, L., Li, L., Zheng, J., Fan, X., Yin, F., Shen, H., & Zhang, J. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-017-5581-1 · Cited by 70 · see citing works

Automatic quality assessment for 2D fetal sonographic standard plane based on multitask learning

Zhang, B., Liu, H., Luo, H., & Li, K. (2021) · Medicine · doi:10.1097/md.0000000000024427 · Cited by 70 · see citing works

Multi-Task Weakly-Supervised Attention Network for Dementia Status Estimation With Structural MRI

Lian, C., Liu, M., Wang, L., & Shen, D. G. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3055772 · Cited by 70 · see citing works

Deep multi-scale attentional features for medical image segmentation

Poudel, S., & Lee, S.-W. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107445 · Cited by 70 · see citing works

Deep Learning for Medical Image Cryptography: A Comprehensive Review

Lata, K., & Cenkeramaddi, L. R. (2023) · Applied Sciences · doi:10.3390/app13148295 · Cited by 69 · see citing works

Multi-Hypergraph Learning for Incomplete Multimodality Data

Liu, M., Gao, Y., Yap, P., & Shen, D. G. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2732287 · Cited by 68 · see citing works

Ensemble of deep convolutional neural networks based multi‐modality images for Alzheimer's disease diagnosis

Fang, X., Liu, Z., & Xu, M. (2019) · IET Image Processing · doi:10.1049/iet-ipr.2019.0617 · Cited by 66 · see citing works

Hierarchical Feature Extraction for Early Alzheimer’s Disease Diagnosis

Yue, L., Gong, X., Li, J., Ji, H., Li, M., & Nandi, A. K. (2019) · IEEE Access · doi:10.1109/access.2019.2926288 · Cited by 63 · see citing works

Automatic 3D landmarking model using patch‐based deep neural networks for CT image of oral and maxillofacial surgery

Ma, Q., Kobayashi, E., Fan, B., Nakagawa, K., Sakuma, I., Masamune, K., & Suenaga, H. (2020) · International Journal of Medical Robotics and Computer Assisted Surgery · doi:10.1002/rcs.2093 · Cited by 62 · see citing works

A Registration and Deep Learning Approach to Automated Landmark Detection for Geometric Morphometrics

Devine, J., Aponte, J. D., Katz, D. C., Liu, W., Vercio, L. L., Forkert, N. D., Marcucio, R., Percival, C. J., & Hallgrímsson, B. (2020) · Evolutionary Biology · doi:10.1007/s11692-020-09508-8 · Cited by 59 · see citing works

An ultrasound standard plane detection model of fetal head based on multi-task learning and hybrid knowledge graph

Zhao, L., Li, K., Pu, B., Chen, J., Li, S., & Liao, X. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.04.011 · Cited by 58 · see citing works

Automatic Construction of Chinese Herbal Prescriptions From Tongue Images Using CNNs and Auxiliary Latent Therapy Topics

Hu, Y., Wen, G., Liao, H., Wang, C., Dai, D., & Yu, Z. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2909925 · Cited by 58 · see citing works

Effectiveness of Human–Artificial Intelligence Collaboration in Cephalometric Landmark Detection

Le, V. N. T., Kang, J., Oh, I.-S., Kim, J., Yang, Y.-M., & Lee, D. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12030387 · Cited by 56 · see citing works

Superficial white matter analysis: An efficient point-cloud-based deep learning framework with supervised contrastive learning for consistent tractography parcellation across populations and dMRI acquisitions

Xue, T., Zhang, F., Zhang, C., Chen, Y., Song, Y., Golby, A. J., Makris, N., Yogesh Rathi, A., Cai, W., & O’Donnell, L. J. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102759 · Cited by 56 · see citing works

GaNDLF: the generally nuanced deep learning framework for scalable end-to-end clinical workflows

Pati, S., Thakur, S. P., Hamamcı, İ. E., Baid, U., Baheti, B., Bhalerao, M., Güley, O., Mouchtaris, S., Lang, D., Thermos, S., et al. (2023) · Communications Engineering · doi:10.1038/s44172-023-00066-3 · Cited by 54 · see citing works

Region-Adaptive Deformable Registration of CT/MRI Pelvic Images via Learning-Based Image Synthesis

Cao, X., Yang, J., Gao, Y., Wang, Q., & Shen, D. G. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2820424 · Cited by 51 · see citing works

Structure-Aware Long Short-Term Memory Network for 3D Cephalometric Landmark Detection

Chen, R., Ma, Y., Chen, N., Liu, L., Cui, Z., Lin, Y., & Wang, W. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3149281 · Cited by 48 · see citing works

Deep learning algorithms to isolate and quantify the structures of the anterior segment in optical coherence tomography images

Pham, T. H., Devalla, S. K., Ang, A. J. F., Soh, Z. D., Thiéry, A. H., Boote, C., Cheng, C.-Y., Girard, M. J. A., & Koh, V. (2020) · British Journal of Ophthalmology · doi:10.1136/bjophthalmol-2019-315723 · Cited by 48 · see citing works

Artificial intelligence system for automated landmark localization and analysis of cephalometry

Jiang, F., Guo, Y., Yang, C., Zhou, Y., Lin, Y., Cheng, F., Quan, S., Feng, Q., & Li, J. (2022) · Dentomaxillofacial Radiology · doi:10.1259/dmfr.20220081 · Cited by 47 · see citing works

Machine learning in medical imaging

Kumar, A., Bi, L., Kim, J., & Feng, D. D. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-816034-3.00005-5 · Cited by 47 · see citing works

Localization of Craniomaxillofacial Landmarks on CBCT Images Using 3D Mask R-CNN and Local Dependency Learning

Lang, Y., Lian, C., Xiao, D., Deng, H., Thung, K., Yuan, P., Gateño, J., Kuang, T., Alfi, D. M., Wang, L., et al. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3174513 · Cited by 47 · see citing works

Patch-based deep multi-modal learning framework for Alzheimer’s disease diagnosis using multi-view neuroimaging

Liu, F., Yuan, S., Li, W., Xu, Q. J., & Sheng, B. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104400 · Cited by 45 · see citing works

Fast Multiple Landmark Localisation Using a Patch-Based Iterative Network

Li, Y., Alansary, A., Cerrolaza, J. J., Khanal, B., Sinclair, M. D., Matthew, J., Gupta, C., Knight, C. L., Kainz, B., & Rueckert, D. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00928-1_64 · Cited by 45 · see citing works

HeadLocNet: Deep convolutional neural networks for accurate classification and multi-landmark localization of head CTs

Zhang, D., Wang, J., Noble, J. H., & Dawant, B. M. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101659 · Cited by 39 · see citing works

Automatic Detection of the Aortic Annular Plane and Coronary Ostia from Multidetector Computed Tomography

Astudillo, P., Mortier, P., Bosmans, J. M., Backer, O. D., de Jaegere, P. P. T., Iannaccone, F., Beule, M. D., & Dambre, J. (2020) · Journal of Interventional Cardiology · doi:10.1155/2020/9843275 · Cited by 38 · see citing works

Deep learning methods for early detection of Alzheimer's disease using structural MR images: a survey

Hassen, S. B., Neji, M., Hussain, Z. U., Hussain, A., Alimi, A. M., & Frikha, M. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127325 · Cited by 30 · see citing works

Landmarks Detection with Anatomical Constraints for Total Hip Arthroplasty Preoperative Measurements

Liu, W., Wang, Y., Jiang, T., Chi, Y., Lei Zhang, A., & Hua, X. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59719-1_65 · Cited by 32 · see citing works

Augmented reality for inner ear procedures: visualization of the cochlear central axis in microscopic videos

Hussain, R., Lalande, A., Girum, K. B., Guigou, C., & Grayeli, A. B. (2020) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-020-02240-w · Cited by 31 · see citing works

CNN-based Landmark Detection in Cardiac CTA ScansPreprint

Noothout, J. M. H., de Vos, B. D., Wolterink, J. M., Leiner, T., & Išgum, I. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.04963 · Cited by 31 · see citing works

Comparative Analysis of Resource-Efficient CNN Architectures for Brain Tumor Classification

Khan, M., & Auvee, R. B. Z. (2024) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit64611.2024.11021970 · Cited by 30 · see citing works

3D Inception-based CNN with sMRI and MD-DTI data fusion for Alzheimer's Disease diagnosticsPreprint

Khvostikov, A. V., Aderghal, K., Krylov, A. S., Catheline, G., & Benois‐Pineau, J. (2018) · arXiv (Cornell University) · doi:10.13140/rg.2.2.30737.28006 · Cited by 29 · see citing works

Topological correction of infant white matter surfaces using anatomically constrained convolutional neural network

Sun, L., Zhang, D., Lian, C., Wang, L., Wu, Z., Shao, W., Lin, W., Shen, D. G., & Li, G. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.05.037 · Cited by 26 · see citing works

Low-Rank Representation for Multi-center Autism Spectrum Disorder Identification

Wang, M., Zhang, D., Huang, J., Shen, D. G., & Liu, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00928-1_73 · Cited by 25 · see citing works

A deep learning framework for prostate localization in cone beam CT‐guided radiotherapy

Liang, X., Zhao, W., Hristov, D. H., Buyyounouski, M. K., Hancock, S. L., Bagshaw, H. P., Zhang, Q., Xie, Y., & Xing, L. (2020) · Medical Physics · doi:10.1002/mp.14355 · Cited by 25 · see citing works

Automated detection, selection and classification of hippocampal landmark points for the diagnosis of Alzheimer’s disease

Poloni, K. M., & Ferrari, R. J. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106581 · Cited by 23 · see citing works

Introduction to Machine Learning

Morr, C. E., Jammal, M., Ali‐Hassan, H., & El-Hallak, W. (2022) · International series in management science/operations research/International series in operations research & management science · doi:10.1007/978-3-031-16990-8_1 · Cited by 22 · see citing works

AIR-Net: Acupoint image registration network for automatic acupoint recognition and localization

Li, Y., Teng, Y., Huang, Y., Huang, L., Yang, S., Liu, J., Zou, H., & Xie, Y. (2024) · Displays · doi:10.1016/j.displa.2024.102743 · Cited by 21 · see citing works

Breast tumor segmentation in DCE-MRI using fully convolutional networks with an application in radiogenomics

Zhang, J., Saha, A., Zhu, Z., & Mazurowski, M. A. (2018) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.2295436 · Cited by 21 · see citing works

Robust Fovea Detection in Retinal OCT Imaging Using Deep Learning

Schurer-Waldheim, S., Seeböck, P., Bogunović, H., Gerendas, B. S., & Schmidt-Erfurth, U. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3166068 · Cited by 20 · see citing works

Uncertainty Estimation for Heatmap-Based Landmark Localization

Schobs, L., Swift, A. J., & Lu, H. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3222730 · Cited by 20 · see citing works

Using a New Deep Learning Method for 3D Cephalometry in Patients With Cleft Lip and Palate

Xu, M., Liu, B., Luo, Z., Ma, H., Sun, M., Wang, Y., Yin, N., Tang, X., & Song, T. (2023) · Journal of Craniofacial Surgery · doi:10.1097/scs.0000000000009299 · Cited by 20 · see citing works

Multi-input adaptive neural network for automatic detection of cervical vertebral landmarks on X-rays

Wang, Y., Huang, L., Wu, M., Liu, S., Jiao, J., & Bai, T. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105576 · Cited by 19 · see citing works

Facial Anatomical Landmark Detection Using Regularized Transfer Learning With Application to Fetal Alcohol Syndrome Recognition

Fu, Z., Jiao, J., Suttie, M., & Noble, J. A. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3110680 · Cited by 19 · see citing works

A configurable deep learning framework for medical image analysis

Chen, J., Yang, N., Zhou, M., Zhang, Z., & Yang, X. (2022) · Neural Computing and Applications · doi:10.1007/s00521-021-06873-z · Cited by 18 · see citing works

TANet: Triple Attention Network for medical image segmentation

Wei, X., Ye, F., Wan, H., Xu, J., & Min, W. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104608 · Cited by 18 · see citing works

Anatomical landmark localization via convolutional neural networks for limb-length discrepancy measurements

Tsai, A. (2021) · Pediatric Radiology · doi:10.1007/s00247-021-05004-z · Cited by 18 · see citing works

Discriminative Feature Network Based on a Hierarchical Attention Mechanism for Semantic Hippocampus Segmentation

Shi, J., Zhang, R., Guo, L., Gao, L., Ma, H., & Wang, J. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2994114 · Cited by 16 · see citing works

Joint Neuroimage Synthesis and Representation Learning for Conversion Prediction of Subjective Cognitive Decline

Liu, Y., Pan, Y., Yang, W., Ning, Z., Yue, L., Liu, M., & Shen, D. G. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59728-3_57 · Cited by 16 · see citing works

DLLNet: An Attention-Based Deep Learning Method for Dental Landmark Localization on High-Resolution 3D Digital Dental Models

Lang, Y., Deng, H., Xiao, D., Lian, C., Kuang, T., Gateño, J., Yap, P., & Xia, J. J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87202-1_46 · Cited by 16 · see citing works

Contrastive Learning of Relative Position Regression for One-Shot Object Localization in 3D Medical Images

Lei, W., Xu, W., Gu, R., Fu, H., Zhang, S., Zhang, S., & Wang, G. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87196-3_15 · Cited by 16 · see citing works

Point detection through multi-instance deep heatmap regression for sutures in endoscopy

Sharan, L., Romano, G., Brand, J., Kelm, H., Karck, M., Simone, R. D., & Engelhardt, S. (2021) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-021-02523-w · Cited by 15 · see citing works

Multi-task hourglass network for online automatic diagnosis of developmental dysplasia of the hip

Xu, J., Xie, H., Tan, Q., Wu, H. Y., Liu, C., Zhang, S., Mao, Z., & Zhang, Y. (2022) · World Wide Web · doi:10.1007/s11280-022-01051-0 · Cited by 15 · see citing works

Accurate Landmark Localization for Medical Images Using Perturbations

Kang, J., Oh, K., & Oh, I.-S. (2021) · Applied Sciences · doi:10.3390/app112110277 · Cited by 14 · see citing works

Multi-task joint learning network based on adaptive patch pruning for Alzheimer’s disease diagnosis and clinical score prediction

Liu, F., Yuan, S., Li, W., Xu, Q., Wu, X. C., Han, K., Wang, J., & Miao, S. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106398 · Cited by 14 · see citing works

End-to-End Coordinate Regression Model with Attention-Guided Mechanism for Landmark Localization in 3D Medical Images

Li, J., Wang, Y., Mao, J., Li, G., & Ma, R. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59861-7_63 · Cited by 14 · see citing works

Landmark Localisation in Radiographs Using Weighted Heatmap Displacement Voting

Davison, A. K., Lindner, C., Perry, D. C., Luo, W., Collaborative, M. S. A., & Cootes, T. F. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11166-3_7 · Cited by 14 · see citing works

Left ventricle landmark localization and identification in cardiac MRI by deep metric learning-assisted CNN regression

Wang, X., Zhai, S., & Niu, Y. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.02.069 · Cited by 13 · see citing works

Deep Learning-Based Landmark Localisation in the Liver for Couinaud Segmentation

Arya, Z., Ridgway, G. R., Jandor, A., & Aljabar, P. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-80432-9_18 · Cited by 13 · see citing works

Exploring Intrinsic Discrimination and Consistency for Weakly Supervised Object Localization

Wang, C., Xu, R., Xu, S., Meng, W., Wang, R., & Zhang, X. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3356174 · Cited by 12 · see citing works

PELE scores: pelvic X-ray landmark detection with pelvis extraction and enhancement

Huang, Z. Y., Li, H., Shao, S., Zhu, H., Hu, H., Cheng, Z., Wang, J., & Zhou, S. K. (2024) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-024-03089-z · Cited by 11 · see citing works

Automatic coronary artery lumen segmentation in computed tomography angiography using paired multi-scale 3D CNN

Chen, F., Li, Y., Tian, T., Cao, F., & Liang, J. (2018) · SPIE Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging · doi:10.1117/12.2293289 · Cited by 11 · see citing works

MSANet: Multi-scale attention networks for image classification

Cao, P., Xie, F., Zhang, S., Zhang, Z., & Zhang, J. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12792-5 · Cited by 11 · see citing works

Mask R-CNN assisted 2.5D object detection pipeline of 68Ga-PSMA-11 PET/CT-positive metastatic pelvic lymph node after radical prostatectomy from solely CT imaging

Xu, D., Ma, M., Cao, M., Kishan, A. U., Nickols, N. G., Scalzo, F., & Sheng, K. (2023) · Scientific Reports · doi:10.1038/s41598-023-28669-y · Cited by 11 · see citing works

An Introduction to Deep Learning Applications In MRI Images

Datta, P., & Rohilla, R. (2019) · International Conference on Power Energy, Environment and Intelligent Control (PEEIC) · doi:10.1109/peeic47157.2019.8976727 · Cited by 11 · see citing works

Automatic Assessment of Pectus Excavatum Severity From CT Images Using Deep Learning

Silva, B., Pessanha, I., Correia-Pinto, J., Fonseca, J. C., & Queirós, S. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3090966 · Cited by 11 · see citing works

DeepNavNet: Automated Landmark Localization for Neuronavigation

Edwards, C. A., Goyal, A., Rusheen, A. E., Kouzani, A. Z., & Lee, K. H. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.670287 · Cited by 11 · see citing works

Unsupervised Domain Adaptation for Multi-Center Autism Spectrum Disorder Identification

Jiang, Y., Li, Z., & Zhang, D. (2019) · IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) · doi:10.1109/smartworld-uic-atc-scalcom-iop-sci.2019.00287 · Cited by 10 · see citing works

Feature Aggregation and Refinement Network for 2D Anatomical Landmark Detection

Ao, Y., & Wu, H. Q. (2022) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-022-00718-4 · Cited by 36 · see citing works

Anatomical Landmark Detection using Deep Appearance-Context Network

Reddy, P. K., Kanakatte, A., Gubbi, J., Poduval, M., Ghose, A., & Purushothaman, B. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9630457 · Cited by 15 · see citing works

Anatomical Landmark Detection

Liu, D., & Zhou, S. K. (2013) · Springer eBooks · doi:10.1007/978-1-4614-8498-1_27

Volumetric Landmark Detection with a Multi-Scale Shift Equivariant Neural Network

Ma, T., Gupta, A. K., & Sabuncu, M. R. (2020) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi45749.2020.9098620 · Cited by 13 · see citing works

Learn Fine-Grained Adaptive Loss for Multiple Anatomical Landmark Detection in Medical Images

Zhou, G.-Q., Miao, J., Yang, X., Li, R., Huo, E.-Z., Shi, W., Huang, Y., Qian, J., Chen, C., & Ni, D. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3080703 · Cited by 29 · see citing works