Fully convolutional networks for multi-modality isointense infant brain image segmentation

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148

Gradient-based learning applied to document recognition

Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805

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

Untitled work

(2010) · Cited by 14,134

Fully convolutional networks for semantic segmentation

Shelhamer, E., Long, J., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572683 · Cited by 11,898

Caffe: Convolutional Architecture for Fast Feature Embedding

Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077

Multimodal Learning with Deep Boltzmann Machines

Srivastava, N., & Salakhutdinov, R. (2012) · doi:10.5555/2627435.2697059 · Cited by 1,070

Deep convolutional neural networks for multi-modality isointense infant brain image segmentation

Zhang, W., Li, R., Deng, H., Wang, L., Lin, W., Ji, S., & Shen, D. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.12.061 · Cited by 817

Adaptive, template moderated, spatially varying statistical classification

Warfield, S. K., Kaus, M. R., Jolesz, F. A., & Kikinis, R. (2000) · Medical Image Analysis · doi:10.1016/s1361-8415(00)00003-7 · Cited by 402

Automatic segmentation of MR images of the developing newborn brain

Prastawa, M. W., Gilmore, J. H., Lin, W., & Gerig, G. (2005) · Medical Image Analysis · doi:10.1016/j.media.2005.05.007 · Cited by 328

Segmentation of Craniomaxillofacial Bony Structures from MRI with a 3D Deep-Learning Based Cascade Framework

Nie, D., Wang, L., Trullo, R., Li, J., Yuan, P., Xia, J. J., & Shen, D. G. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67389-9_31 · Cited by 26

Deep Learning in Multimodal Medical Image Analysis

Xu, Y. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-32962-4_18 · Cited by 32

Comprehensive computer‐aided diagnosis for breast T1‐weighted DCE‐MRI through quantitative dynamical features and spatio‐temporal local binary patterns

Piantadosi, G., Marrone, S., Fusco, R., Sansone, M., & Sansone, C. (2018) · IET Computer Vision · doi:10.1049/iet-cvi.2018.5273 · Cited by 26

Uncertainty-Aware Multi-modal Learning via Cross-Modal Random Network Prediction

Wang, H., Zhang, J., Chen, Y., Ma, C., Avery, J., Hull, M. L., & Carneiro, G. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19836-6_12 · Cited by 27

Segmentation-Assisted Fully Convolutional Neural Network Enhances Deep Learning Performance to Identify Proliferative Diabetic Retinopathy

Alam, M. N., Zhao, E., Lam, C., & Rubin, D. L. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12010385 · Cited by 26

Utilization of a convolutional method for Alzheimer disease diagnosis

Allioui, H., Sadgal, M., & Fazziki, A. E. (2020) · Machine Vision and Applications · doi:10.1007/s00138-020-01074-5 · Cited by 25

An Attentive Multi-Modal CNN for Brain Tumor Radiogenomic Classification

Qu, R., & Xiao, Z. (2022) · Information · doi:10.3390/info13030124 · Cited by 23

In-Bed Human Pose Estimation from Unseen and Privacy-Preserving Image Domains

Cao, T., Armin, M. A., Denman, S., Petersson, L., & Ahmedt‐Aristizabal, D. (2022) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi52829.2022.9761598 · Cited by 22

Saliency and ballness driven deep learning framework for cell segmentation in bright field microscopic images

Asha, S. B., Gopakumar, G., & Gorthi, R. K. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105704 · Cited by 22

Modality preserving U-Net for segmentation of multimodal medical images

Wu, B., Zhang, F., Xu, L., Shen, S., Shao, P., Sun, M., Liu, P., Yao, P., & Xu, R. X. (2023) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-22-1367 · Cited by 21

A survey of MRI-based brain tissue segmentation using deep learning

Wu, L., Wang, S., Liu, J., Hou, L., Li, N., Su, F., Yang, X., Lu, W., Qiu, J., Zhang, M., et al. (2024) · Complex & Intelligent Systems · doi:10.1007/s40747-024-01639-1 · Cited by 21

Narrow Band Active Contour Attention Model for Medical Segmentation

Le, N., Bui, T., Vo-Ho, V.-K., Yamazaki, K., & Luu, K. (2021) · Diagnostics · doi:10.3390/diagnostics11081393 · Cited by 20

Multi-Atlas Brain Parcellation Using Squeeze-and-Excitation Fully Convolutional Networks

Tang, Z., Liu, X., Li, Y., Yap, P., & Shen, D. G. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2994445 · Cited by 20

Learning U-Net Based Multi-Scale Features in Encoding-Decoding for MR Image Brain Tissue Segmentation

Long, J., Ma, G., Song, E., & Jin, R. (2021) · Sensors · doi:10.3390/s21093232 · Cited by 20

Integrating multiple MRI sequences for pelvic organs segmentation via the attention mechanism

Huang, S., Cheng, Z., Lai, L., Zheng, W., He, M., Li, J., Zeng, T., Huang, X., & Yang, X. N. (2021) · Medical Physics · doi:10.1002/mp.15285 · Cited by 20

Deep 3D Neural Network for Brain Structures Segmentation Using Self-Attention Modules in MRI Images

Laiton-Bonadiez, C., Torres, G. S., & Branch, J. W. (2022) · Sensors · doi:10.3390/s22072559 · Cited by 20

Automated multi-modal Transformer network (AMTNet) for 3D medical images segmentation

Zheng, S., Tan, J., Jiang, C., & Li, L. (2022) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aca74c · Cited by 20

Multi stream 3D hyper-densely connected network for multi modality isointense infant brain MRI segmentation

Qamar, S., Jin, H., Zheng, R., & Ahmad, P. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-07829-1 · Cited by 20

Deep Learning for Brain Tumor Segmentation

Munir, K., Frezza, F., & Rizzi, A. (2020) · Studies in computational intelligence · doi:10.1007/978-981-15-6321-8_11 · Cited by 19

BIBSNet: A deep learning baby image brain segmentation network for MRI scans

Hendrickson, T. J., Reiners, P., Moore, L. A., Lundquist, J. T., Fayzullobekova, B., Perrone, A. J., Lee, E. G., Moser, J., Day, T. K. M., Alexopoulos, D., et al. (2026) · Developmental Cognitive Neuroscience · doi:10.1101/2023.03.22.533696 · Cited by 24

A deep dense residual network with reduced parameters for volumetric brain tissue segmentation from MR images

Basnet, R., Ahmad, M. O., & Swamy, M. N. S. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103063 · Cited by 19

Medikal Görüntü İşlemede Derin Öğrenme Uygulamaları

Eker, A. G., & Duru, N. J. (2021) · Acta Infologica · doi:10.26650/acin.927561 · Cited by 19

Medical Image Segmentation Using Deep Learning: A Survey

Oubaalla, A., Moubtahij, H. E., & Akkad, N. E. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-29860-8_97 · Cited by 19

Unified Attentional Generative Adversarial Network for Brain Tumor Segmentation from Multimodal Unpaired Images

Yuan, W., Wei, J., Wang, J., Ma, Q., & Taşdizen, T. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32248-9_26 · Cited by 19

Deep Class-Specific Affinity-Guided Convolutional Network for Multimodal Unpaired Image Segmentation

Chen, J., Li, W., Li, H., & Zhang, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59719-1_19 · Cited by 18

Adaptive Cross-Feature Fusion Network With Inconsistency Guidance for Multi-Modal Brain Tumor Segmentation

Yue, G., Zhuo, G., Zhou, T., Liu, W., Wang, T., & Jiang, Q. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3347556 · Cited by 20

MhURI:A Supervised Segmentation Approach to Leverage Salient Brain Tissues in Magnetic Resonance Images

Ghosal, P., Chowdhury, T., Kumar, A., Bhadra, A. K., Chakraborty, J., & Nandi, D. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105841 · Cited by 19

Myocardial Pathology Segmentation of Multi-modal Cardiac MR Images with a Simple but Efficient Siamese U-shaped Network

Li, W., Wang, L., Li, F., Qin, S., & Xiao, B. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103174 · Cited by 17

Mark-Guided Segmentation of Ultrasonic Thyroid Nodules Using Deep Learning

Zhou, S., Wu, H. Q., Gong, J., Le, T., Wu, H., Chen, Q., & Xu, Z. (2018) · International Symposium on Image Computing and Digital Medicine (ISICDM) · doi:10.1145/3285996.3286001 · Cited by 16

Deeply supervised full convolution network for HEp-2 specimen image segmentation

Xie, H., Lei, H., He, Y., & Lei, B. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.03.067 · Cited by 16

Neural Segmentation of Seeding ROIs (sROIs) for Pre-Surgical Brain Tractography

Avital, I., Nelkenbaum, I., Tsarfaty, G., Konen, E., Kiryati, N., & Mayer, A. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2954477 · Cited by 15

A Dual-Stream Centerline-Guided Network for Segmentation of the Common and Internal Carotid Arteries From 3D Ultrasound Images

Jiang, M., & Chiu, B. C. Y. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3263537 · Cited by 15

Quaternion Cross-Modality Spatial Learning for Multi-Modal Medical Image Segmentation

Chen, J., Huang, G., Yuan, X., Zhong, G., Zheng, Z., Pun, C., Zhu, J. L., & Huang, Z. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3346529 · Cited by 15

Multi-modal evidential fusion network for trustworthy PET/CT tumor segmentation

Qi, Y., Lin, L., Zhang, B., Zhang, J., & Wang, J. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113838 · Cited by 15

Multi-atlas active contour segmentation method using template optimization algorithm

Wang, M., Li, P., & Liu, F. (2019) · BMC Medical Imaging · doi:10.1186/s12880-019-0340-6 · Cited by 14

Edge-Guided Near-Eye Image Analysis for Head Mounted Displays

Wang, Z., Zhao, Y., Liu, Y., & Lu, F. (2021) · Proceedings - International Symposium on Mixed and Augmented Reality, ISMAR · doi:10.1109/ismar52148.2021.00015 · Cited by 14

Segmentation of rectal tumor from multi-parametric MRI images using an attention-based fusion network

Dou, M., Chen, Z., Tang, Y., Sheng, L., Zhou, J., Wang, X., & Yao, Y. (2023) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-023-02828-9 · Cited by 14

Deep convolutional encoder-decoder for myelin and axon segmentation

Mesbah, R., Brendan Mccane, A., & Mills, S. J. (2016) · International Conference on Image and Vision Computing New Zealand (IVCNZ) · doi:10.1109/ivcnz.2016.7804455 · Cited by 14

An Effective MR-Guided CT Network Training for Segmenting Prostate in CT Images

Yang, W., Shi, Y., Park, S. H., Yang, M., Gao, Y., & Shen, D. G. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2960153 · Cited by 14

MRI image analysis for cerebrum tumor detection and feature extraction using 2D U-ConvNet and SVM classification

Pitchai, R., Supraja, P., Sulthana, A. R., Veeramakali, T., & Babu, C. M. (2022) · Personal and Ubiquitous Computing · doi:10.1007/s00779-022-01676-y · Cited by 13

3D‐MASNet : 3D mixed‐scale asymmetric convolutional segmentation network for 6‐month‐old infant brain MR images

Zeng, Z., Zhao, T., Sun, L., Zhang, Y., Xia, M., Liao, X., Zhang, J., Shen, D. G., Wang, L., & Yong He, A. (2022) · Human Brain Mapping · doi:10.1002/hbm.26174 · Cited by 12

SM-SegNet: A Lightweight Squeeze M-SegNet for Tissue Segmentation in Brain MRI Scans

Yamanakkanavar, N., Choi, J. Y., & Lee, B. (2022) · Sensors · doi:10.3390/s22145148 · Cited by 14

Human Brain Tissue Segmentation in fMRI using Deep Long-Term Recurrent Convolutional Network

Ang, S. P., Phung, S. L., Schira, M. M., Bouzerdoum, A., & Duong, S. T. M. (2018) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2018.8615850 · Cited by 12

GSCFN: A graph self-construction and fusion network for semi-supervised brain tissue segmentation in MRI

Zhang, Y., Li, Y., Kong, Y., Wu, J., Yang, J., Shu, H., & Coatrieux, G. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.05.047 · Cited by 12

Evaluating the relationship between magnetic resonance image quality metrics and deep learning‐based segmentation accuracy of brain tumors

Rajarajeswari, M., Celaya, A., Yung, J. P., Long, J. P., Viswanath, S. E., Marcus, D. S., Chung, C. D., & Fuentes, D. (2024) · Medical Physics · doi:10.1002/mp.17059 · Cited by 13

Brain Image Labeling Using Multi-atlas Guided 3D Fully Convolutional Networks

Fang, L., Zhang, L., Nie, D., Cao, X., Bahrami, K., He, H., & Shen, D. G. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67434-6_2 · Cited by 11

Age-specific optimization of T1-weighted brain MRI throughout infancy

Zhang, H., Lai, C., Liu, R., Liu, T., Niu, W., Oishi, K., Zhang, Y., & Wu, D. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.05.075 · Cited by 12

Segmentation of white matter hyperintensities on 18F-FDG PET/CT images with a generative adversarial network

Oh, K. T., Kim, D., Ye, B. S., Lee, S., Yun, M., & Yoo, S. K. (2021) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-021-05285-4 · Cited by 11

Attention-modulated multi-branch convolutional neural networks for neonatal brain tissue segmentation

Fan, X., Shan, S., Li, X., Li, J., Mi, J., Yang, J., & Zhang, Y. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105522 · Cited by 11

Deep HT: A deep neural network for diagnose on MR images of tumors of the hand

Hu, X., Liu, Z., Zhou, H., Fang, J., & Lü, H. (2020) · PLoS ONE · doi:10.1371/journal.pone.0237606 · Cited by 11

UMSCS: A Novel Unpaired Multimodal Image Segmentation Method Via Cross-Modality Generative and Semi-supervised Learning

Yang, F., Li, X., Wang, B., Teng, P., & Liu, G. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02389-4 · Cited by 11

Improved Brain Segmentation Using Pixel Separation and Additional Segmentation Features

Khaled, A., Own, C., Tao, W., & Ghaleb, T. A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60290-1_7 · Cited by 10

Medical lesion segmentation by combining multimodal images with modality weighted UNet

Zhu, X., Wu, Y., Hu, H. R., Zhuang, X., Yao, J., Ou, D., Li, W., Song, M. L., Feng, N., & Xu, D. M. (2022) · Medical Physics · doi:10.1002/mp.15610 · Cited by 11

Adult-Like Phase and Multi-scale Assistance for Isointense Infant Brain Tissue Segmentation

Liu, J., Liu, F., Sun, K., Liu, M., Sun, Y., Ge, Y., & Shen, D. G. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43901-8_6 · Cited by 10

Robust 3D Convolutional Neural Network With Boundary Correction for Accurate Brain Tissue Segmentation

Hou, B., Kang, G., Zhang, N., & Hu, C. (2018) · IEEE Access · doi:10.1109/access.2018.2882848 · Cited by 10

IAS‐NET: Joint intraclassly adaptive GAN and segmentation network for unsupervised cross‐domain in neonatal brain MRI segmentation

Li, B., You, X., Wang, J., Peng, Q., Yin, S. W., Qi, R., Ren, Q., & Hong, Z. (2021) · Medical Physics · doi:10.1002/mp.15212 · Cited by 10

Harmonized neonatal brain MR image segmentation model for cross-site datasets

Chen, J., Sun, Y., Fang, Z., Lin, W., Li, G., & Wang, L. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102810 · Cited by 10

Quantitative Structural Brain Magnetic Resonance Imaging Analyses: Methodological Overview and Application to Rett Syndrome

Shiohama, T., & Tsujimura, K. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.835964 · Cited by 10

Image Based Brain Segmentation: From Multi-Atlas Fusion to Deep Learning

Lin, X., & Xiao-xi, L. (2018) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/1573405614666180817125454 · Cited by 10

Deep learning techniques for isointense infant brain tissue segmentation: a systematic literature review

Mhlanga, S. T., & Viriri, S. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1240360 · Cited by 9

Reinforcement tokenization and graph convolution for high-precision breast tumor segmentation in DCE-MRI

Akbar, A., Han, S., Rehman, N. U., Irshad, R., Ahmed, K., Ali, M. M., & Mazroa, A. A. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106947 · Cited by 9

Multi-Modality Reconstruction Attention and Difference Enhancement Network for Brain MRI Image Segmentation

Zhang, X., Liu, Y., Zhang, Q., & Yuan, F. (2022) · IEEE Access · doi:10.1109/access.2022.3156898 · Cited by 9

SIP-UNet: Sequential Inputs Parallel UNet Architecture for Segmentation of Brain Tissues from Magnetic Resonance Images

Prajapati, R., & Kwon, G. (2022) · Mathematics · doi:10.3390/math10152755 · Cited by 9

Learning to detect boundary information for brain image segmentation

Khaled, A., Han, J.-J., & Ghaleb, T. A. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04882-w · Cited by 9

Prostate cancer segmentation from MRI by a multistream fusion encoder

Jiang, M., Yuan, B., Kou, W., Wen, Y., Marshall, H. R., Yang, Q., Syer, T., Punwani, S., Emberton, M., Barratt, D. C., et al. (2023) · Medical Physics · doi:10.1002/mp.16374 · Cited by 9

Segmentation of MR Images using DN Convolutional Neural Network

Bhairnallykar, S. T., & Narawade, V. E. (2023) · International Journal of Information Technology · doi:10.1007/s41870-023-01461-x · Cited by 11

Segmentation of fused MR and CT images using DL-CNN with PGK and NLEM filtered AACGK-FCM

Reddy, M. G., Reddy, P. V. N., & Reddy, P. R. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102618 · Cited by 9

Motion sensor data anonymization by time-frequency filtering

Debs, N., Jourdan, T., Moukadem, A., Boutet, A., & Frindel, C. (2020) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco47968.2020.9287683 · Cited by 9

Artificial intelligence in fetal brain imaging: Advancements, challenges, and multimodal approaches for biometric and structural analysis

Wang, L., Fatemi, M., & Alizad, A. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110312 · Cited by 12

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Liu, Q., Zhu, Y., Liu, M., Yao, Z., Wang, Y., & Meijering, E. H. W. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3593297 · Cited by 8

An Overview of Neural Network Architectures for Healthcare

Zion, I., Ozuomba, S., & Asuquo, P. M. (2020) · International Conference in Mathematics, Computer Engineering and Computer Science (ICMCECS) · doi:10.1109/icmcecs47690.2020.246980 · Cited by 11

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Li, J., Chen, H., Li, Y., & Peng, Y. (2019) · International Conference on Artificial Intelligence and Advanced Manufacture (AIAM) · doi:10.1145/3358331.3358400 · Cited by 8

Analysis of the nonperfused volume ratio of adenomyosis from MRI images based on fewshot learning

Li, J., Wang, W., Liao, L., & Liu, X. (2020) · Physics in Medicine and Biology · doi:10.1088/1361-6560/abd66b · Cited by 8

Strong semantic segmentation for Covid-19 detection: Evaluating the use of deep learning models as a performant tool in radiography

Allioui, H., Mourdi, Y., & Sadgal, M. (2022) · Radiography · doi:10.1016/j.radi.2022.10.010 · Cited by 9

Segmenting localized corrosion from rust-removed metallic surface with deep learning algorithm

Zhang, S., Li, Z., Yang, C., & Zhu, C. (2019) · Journal of Electronic Imaging · doi:10.1117/1.jei.28.4.043019 · Cited by 8

Hybrid method combining superpixel, supervised learning, and random walk for glioma segmentation

Mohammed, L. A., Cherfa, A. B., Cherfa, Y., Belkhamsa, N., & Alim-Ferhat, F. (2020) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22499 · Cited by 7

A Strategy of MR Brain Tissue Images' Suggestive Annotation Based on Modified U-NetPreprint

Deng, Y., Sun, Y., Zhu, Y., Zhu, M., Han, W., & Yuan, K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.07510 · Cited by 10

BAF-Net: bidirectional attention-aware fluid pyramid feature integrated multimodal fusion network for diagnosis and prognosis

Wu, H., Peng, L., Du, D., Xu, H., Lin, G., Zhou, Z., Lu, L., & Lv, W. (2024) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ad3cb2 · Cited by 7

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Kumar, W. K., Devi, K. L., & Singh, A. D. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3222362 · Cited by 7

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Gu, X., Chen, Y., & Tong, W. (2024) · Electronics · doi:10.3390/electronics13234594 · Cited by 7

Auto-segmentation of the parotid glands on MR images of head and neck cancer patients with deep learning strategiesPreprint

Kieselmann, J. P., Fuller, C. D., Gurney‐Champion, O. J., & Oelfke, U. (2020) · medRxiv · doi:10.1101/2020.12.19.20248376 · Cited by 7

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Xiang, D., Peng, T., Bian, Y., Chen, L., Zeng, J., Shi, F., Zhu, W., & Chen, X. (2024) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2024.3467216 · Cited by 6

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Kang, S., Kang, Y., & Tan, S. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3397332 · Cited by 6

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Liu, J., Liu, F., Nie, D., Gu, Y., Sun, Y., & Shen, D. G. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3452310 · Cited by 6

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Bao, N., Zhang, J., Li, Z., Wei, S., Zhang, J., Greenwald, S. E., Onofrey, J. A., Lu, Y., & Xu, L. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3501386 · Cited by 6

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Wu, Y., Xie, Y., & Hong, Y. (2025) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.8386 · Cited by 6

A New Multilevel Feature Fusion Network for Medical Image Segmentation

Qiu, X. (2021) · Sensing and Imaging · doi:10.1007/s11220-021-00346-2 · Cited by 6

Effects of Differential Geometry Parameters on Grid Generation and Segmentation of MRI Brain Image

Zhu, Y., Zhou, Z., Liao, G., Yang, Q., & Yuan, K. (2019) · IEEE Access · doi:10.1109/access.2019.2918265 · Cited by 6

Upgraded W-Net with Attention Gates and Its Application in Unsupervised 3D Liver Segmentation

Mitta, D., Chatterjee, S., Speck, O., & Nürnberger, A. (2021) · DZNE Pub · Cited by 6

Brain Image Parcellation Using Multi-Atlas Guided Adversarial Fully Convolutional Network

Liu, X., Zhao, H., Zhang, S., & Tang, Z. (2019) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2019.8759507 · Cited by 6

Convolutional neural network MRI segmentation for fast and robust optimization of transcranial electrical current stimulation of the human brainPreprint

Sendra-Balcells, C., Salvador, R., Pedro, J., Biagi, M. C., Aubinet, C., Manor, B. D., Thibaut, A., Laureys, S., Lekadir, K., & Ruffini, G. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.29.924985 · Cited by 7

FUNet: Frequency Channel Multi-Modal Fusion and Uncertain Region Adjustment Network for brain tumor segmentation

Yu, Y., Zhang, L., Li, J., Zhang, L., & Zhang, Y. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103474 · Cited by 5

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