Cited by 679Medical Image Segmentation TechniquesIntracerebral and Subarachnoid Hemorrhage ResearchBrain Tumor Detection and Classification

The DOI matches its Crossref record; 49 of the 51 references it cites were found in a registry or an index.

Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks

Dou, Qi, Chen, Hao, Yu, Lequan, Zhao, Lei, Qin, Jing, Wang, Defeng, Mok, Vincent CT, Shi, Lin, Heng, Pheng-Ann·2016·IEEE Transactions on Medical Imaging

DOI 10.1109/tmi.2016.2528129

Open this paper in CoChat — free account

Abstract

Cerebral microbleeds (CMBs) are small haemorrhages nearby blood vessels. They have been recognized as important diagnostic biomarkers for many cerebrovascular diseases and cognitive dysfunctions. In current clinical routine, CMBs are manually labelled by radiologists but this procedure is laborious, time-consuming, and error prone. In this paper, we propose a novel automatic method to detect CMBs from magnetic resonance (MR) images by exploiting the 3D convolutional neural network (CNN). Compared with previous methods that employed either low-level hand-crafted descriptors or 2D CNNs, our method can take full advantage of spatial contextual information in MR volumes to extract more representative high-level features for CMBs, and hence achieve a much better detection accuracy. To further improve the detection performance while reducing the computational cost, we propose a cascaded framework under 3D CNNs for the task of CMB detection. We first exploit a 3D fully convolutional network (FCN) strategy to retrieve the candidates with high probabilities of being CMBs, and then apply a well-trained 3D CNN discrimination model to distinguish CMBs from hard mimics. Compared with traditional sliding window strategy, the proposed 3D FCN strategy can remove massive redundant computations and dramatically speed up the detection process. We constructed a large dataset with 320 volumetric MR scans and performed extensive experiments to validate the proposed method, which achieved a high sensitivity of 93.16% with an average number of 2.74 false positives per subject, outperforming previous methods using low-level descriptors or 2D CNNs by a significant margin. The proposed method, in principle, can be adapted to other biomarker detection tasks from volumetric medical data.

Integrity signals

Registry

Crossref · 10.1109/tmi.2016.2528129

Cites

49 of 49 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

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
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
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
(2008) · Journal of Machine Learning Research · Cited by 33,051
Classification and Regression by randomForest
Liaw, A. I., & Wiener, M. C. (2007) · Cited by 18,594
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
Principal Component Analysis
Jolliffe, I. T. (1986) · Springer series in statistics · doi:10.1007/978-1-4757-1904-8 · Cited by 8,481
Untitled work
(2011) · Cited by 6,993
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382
3D Convolutional Neural Networks for Human Action Recognition
Ji, S., Xu, W., Yang, M., & Yu, K. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.59 · Cited by 6,313

Flagged first, then by citations

Show all 51 references

2 of the 51 references couldn’t be matched to a known paper.

Research works printed but not linked: 4

bengio, 2015, Deep Learning
prasoon, 2013, Medical Image Computing and Comput -Assisted Intervention-MICCAI 2013, 246
roth, 2014, Medical Image Computing and Comput -Assisted Intervention-MICCAI, 520
urban, 2014, Multi-modal brain tumor segmentation using deep convolutional neural networks, Proc Winning Contribution MICCAI BraTS (Brain Tumor Segmentation) Challenge, 31

Sorted by citations

Items 401–500 of 674

Automatic detection System for Degenerative Disk and simulation for artificial disc replacement surgery in the spine
Mahdy, L. N., Ezzat, K. A., & Hassanien, A. E. (2018) · ISA Transactions · doi:10.1016/j.isatra.2018.07.006 · Cited by 9
Applications of Machine Learning in The Field of Medical Care
Dou, H. (2019) · Youth Academic Annual Conference of Chinese Association of Automation (YAC) · doi:10.1109/yac.2019.8787685 · Cited by 9
Automated algorithm for counting microbleeds in patients with familial cerebral cavernous malformations
Zou, X., Hart, B. L., Mabray, M. C., Bartlett, M. R., Bian, W., Nelson, J., Morrison, L. A., McCulloch, C. E., Hess, C. P., Lupo, J., et al. (2017) · Neuroradiology · doi:10.1007/s00234-017-1845-8 · Cited by 9
Multi‐task convolutional neural network‐based design of radio frequency pulse and the accompanying gradients for magnetic resonance imaging
Zhang, Y., Jiang, K., Jiang, W., Wang, N., Wright, A. J., Liu, A., & Wang, J. (2020) · NMR in Biomedicine · doi:10.1002/nbm.4443 · Cited by 8
An efficient stacked bidirectional GRU‐LSTM network for intracranial hemorrhage detection
Kothala, L. P., & Guntur, S. R. (2023) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22958 · Cited by 8
GAN-Based Cross-Modality Brain MRI Synthesis: Paired Versus Unpaired Training and Comparison with Diffusion and Transformer Models
Kalejahi, B. K., Danishvar, S., & Rajabi, M. J. (2026) · Biomimetics · doi:10.3390/biomimetics11030175 · Cited by 8
Towards Accurate Microstructure Estimation via 3D Hybrid Graph Transformer
Yang, J., Jiang, H., Tassew, T. M., Sun, P., Ma, J., Xia, Y., Yap, P., & Chen, G. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43993-3_3 · Cited by 8
Knowledge structure and global trends of machine learning in stroke over the past decade: A scientometric analysis
Wu, M., Yu, K., Zhao, Z., & Zhu, B. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e24230 · Cited by 9
Automated cerebral microbleed detection using selective 3D gradient co-occurance matrix and convolutional neural network
Stanley, B. F., & Franklin, S. W. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103560 · Cited by 8
Fully Automatic Analysis of Muscle B-Mode Ultrasound Images Based on the Deep Residual Shrinkage U-Net
Zheng, W., Zhou, L., Chai, Q.-W., Xu, J., & Liu, S. (2022) · Electronics · doi:10.3390/electronics11071093 · Cited by 8
Automated Detection of Candidate Subjects With Cerebral Microbleeds Using Machine Learning
Sundaresan, V., Arthofer, C., Zamboni, G., Dineen, R. A., Rothwell, P. M., Sotiropoulos, S. N., Auer, D. P., Tozer, D. J., Markus, H. S., Miller, K. L., et al. (2022) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2021.777828 · Cited by 8
A modified residual network for detection and classification of Alzheimer’s disease
Hanoon, F. S., & Alasadi, A. H. H. (2022) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v12i4.pp4400-4407 · Cited by 8
Cerebral microbleed detection in traumatic brain injury patients using 3D convolutional neural networks
Standvoss, K., Goerke, L., Crijns, T., van Niedek, T., Burgos, N. A., Janssen, D., van Vugt, J., Gerritse, E. J., Mol, J. H. Q., van de Vooren, D., et al. (2018) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.2294016 · Cited by 8
Shearlet based Stacked Convolutional Network for Multiclass Diagnosis of Alzheimer’s Disease using the Florbetapir PET Amyloid Imaging Data
Jabason, E., Ahmad, M. O., & Swamy, M. N. S. (2018) · IEEE International New Circuits and Systems Conference (NEWCAS) · doi:10.1109/newcas.2018.8585550 · 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
Classification of glomerular spikes using Convolutional Neural Network
Chen, Y., Li, M., Fang, H., Han, W., Niu, D., & Wang, C. (2020) · Conference on Artificial Intelligence and Healthcare (CAIH) · doi:10.1145/3433996.3434043 · Cited by 8
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
Vertebrae Detection and Localization in CT with Two-Stage CNNs and Dense AnnotationsPreprint
McCouat, J., & Glocker, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.05911 · Cited by 8
Hidden in Plain Sight: A Data-Driven Approach to Safety Risk Management for Highway Traffic Officers
Bortey, L., Edwards, D. J., Roberts, C., & Rillie, I. (2024) · Buildings · doi:10.3390/buildings14113509 · Cited by 8
Automatic cerebral microbleeds detection from MR images via multi-channel and multi-scale CNNs
Khaffafi, B., Khoshakhalgh, H., Keyhanazar, M., & Mostafapour, E. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.109938 · Cited by 7
A Novel U-Net Based Deep Learning Method for 3D Cardiovascular MRI Segmentation
Lu, Y., Zhao, Y., Chen, X., & Guo, X. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/4103524 · Cited by 9
Deep‐Learning‐Based MRI Microbleeds Detection for Cerebral Small Vessel Disease on Quantitative Susceptibility Mapping
Xia, P., Hui, E. S. K., Chua, B. J., Huang, F., Wang, Z., Zhang, H., Yu, H., Lau, K. K., Mak, H. K., & Cao, P. (2023) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.29198 · Cited by 7
FMA-Net: Fusion of Multi-Scale Attention for Grading Cervical Precancerous Lesions
Duan, Z., Xu, C., Li, Z., Feng, B., & Nie, C. (2024) · Mathematics · doi:10.3390/math12070958 · Cited by 7
Efficient False Positive Reduction in Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation
Roth, H. R., Lü, L., Liu, J., Yao, J., Seff, A., Cherry, K. M., Kim, L., & Summers, R. M. (2017) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-319-42999-1_3 · Cited by 8
3D Convolutional Networks Based Automatic Diagnosis of Alzheimer's Disease Using Structural MRI
Yu, X., Peng, B., Shi, J., Zhu, J., & Dai, Y. (2019) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei48845.2019.8965827 · Cited by 7
Dual dense context-aware network for hippocampal segmentation
Shi, J., Zhang, R., Guo, L., Gao, L., Li, Y., Ma, H., & Wang, J. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102038 · Cited by 7
Meta-heuristic Techniques to Train Artificial Neural Networks for Medical Image Classification: A Review
Priyanka, & Kumar, D. (2020) · Recent Advances in Computer Science and Communications · doi:10.2174/2666255813999200915141534 · Cited by 7
Generative Model of Brain Microbleeds for MRI Detection of Vascular Marker of Neurodegenerative Diseases
Momeni, S., Fazlollahi, A., Lebrat, L., Yates, P. A., Rowe, C. C., Gao, Y., Liew, A. W., & Salvado, O. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.778767 · Cited by 7
A Survey on Convolutional Neural Networks for MRI Analysis
Hardaha, S., Edla, D. R., & Parne, S. R. (2022) · Wireless Personal Communications · doi:10.1007/s11277-022-09989-0 · Cited by 8
Loc-VAE: Learning Structurally Localized Representation from 3D Brain MR Images for Content-Based Image Retrieval
Nishimaki, K., Ikuta, K., Onga, Y., Iyatomi, H., & Oishi, K. (2022) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53654.2022.9945411 · Cited by 7
Classification of ischemic and hemorrhagic stroke using Enhanced-CNN deep learning technique
Shakunthala, M., & Helenprabha, K. (2023) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-230024 · Cited by 7
Akciğer nodül özelliklerinin tahmininde çeşitli sınıflama stratejilerinin incelenmesi
Kaya, A., Keçeli, A. S., & Can, A. B. (2018) · Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi · doi:10.17341/gazimmfd.416530 · Cited by 7
G2C: A Generator-to-Classifier Framework Integrating Multi-Stained Visual Cues for Pathological Glomerulus Classification
Wu, B., Zhang, X., Zhao, S., Xie, L., Zeng, C., Liu, Z., & Sun, G. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33011214 · Cited by 7
Automatic lesion detection with three-dimensional convolutional neural networks
Dou, Q., Chen, H., Qin, J., & Heng, P. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-816034-3.00009-2 · Cited by 7
Self-citation
Medical Image Detection Using Deep Learning
Ocaña, M. I. G., López‐Linares, K., Lete, N., Ballester, M. Á. G., & Macía, I. (2019) · Intelligent systems reference library · doi:10.1007/978-3-030-32606-7_1 · Cited by 7
An Efficient False-Positive Reduction System for Cerebral Microbleeds Detection
Afzal, S., Maqsood, M., Mehmood, I., Niaz, M. T., & Seo, S. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2021.013966 · Cited by 7
Fast upper airway magnetic resonance imaging for assessment of speech production and sleep apnea
Kim, Y. (2018) · Precision and Future Medicine · doi:10.23838/pfm.2018.00100 · Cited by 7
Deep-Learning-Based Source Localization Using a Local 3-D Acoustic Intensity Field in a Sound-Speed Mismatched Deep-Ocean Environment
Qu, Y., Sun, J., Yu, J., Tian, Y., & Dong, H. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3424556 · Cited by 6
A Review on Classification and Retrieval of Biomedical Images Using Artificial Intelligence
Greeshma, K. V., & Gripsy, J. V. (2021) · Internet of things · doi:10.1007/978-3-030-75220-0_3 · Cited by 6
Deep multi-scale resemblance network for the sub-class differentiation of adrenal masses on computed tomography images
Bi, L., Kim, J., Su, T., FULHAM, M. J., Feng, D. D., & Ning, G. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102374 · Cited by 6
Quantitative analysis of protein dynamics using a deep learning technique combined with experimental cryo-EM density data and MD simulations
Matsumoto, S., Ishida, S., Terayama, K., & Okuno, Y. (2023) · Biophysics and Physicobiology · doi:10.2142/biophysico.bppb-v20.0022 · Cited by 6
Pattern-Aware Transformer: Hierarchical Pattern Propagation in Sequential Medical Images
Wu, L., Gao, X., Hu, Z., & Zhang, S. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3306468 · Cited by 6
Real-world application of a 3D deep learning model for detecting and localizing cerebral microbleeds
Won, S. Y., Kim, J.-H., Woo, C., Kim, D., Park, K. Y., Kim, E. Y., Baek, S., Han, H. J., & Sohn, B. (2024) · Acta Neurochirurgica · doi:10.1007/s00701-024-06267-9 · Cited by 6
Post-Traumatic Cerebral Microhemorrhages and their Effects Upon White Matter Connectivity in the Aging Human Brain
Fan, D., Chaudhari, N. N., Rostowsky, K. A., Calvillo, M., Lee, S. K., Chowdhury, N. F., Zhang, F., O’Donnell, L. J., & Andrei Irimia, A. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8857921 · Cited by 6
A Robust and Accurate Deep-learning-based Method for the Segmentation of Subcortical Brain: Cross-dataset Evaluation of Generalization Performance
Furuhashi, N., Okuhata, S., & Kobayashi, T. (2020) · Magnetic Resonance in Medical Sciences · doi:10.2463/mrms.mp.2019-0199 · Cited by 6
Determining Top Fully Connected Layer’s Hidden Neuron Count for Transfer Learning, Using Knowledge Distillation: a Case Study on Chest X-Ray Classification of Pneumonia and COVID-19
Ghosh, R. (2021) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-021-00518-2 · Cited by 7
Using transfer learning for automated microbleed segmentation
Dadar, M., Zhernovaia, M., Mahmoud, S., Camicioli, R. M., Maranzano, J., & Duchesne, S. (2022) · Frontiers in Neuroimaging · doi:10.3389/fnimg.2022.940849 · Cited by 6
Knowledge-guided 2.5D CNN for cerebral microbleeds detection
Fang, Z., Zhang, R., Guo, L., Xia, T., Zeng, Y., & Wu, X. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105078 · Cited by 6
Deep learning-assisted IoMT framework for cerebral microbleed detection
Ali, Z., Naz, S., Yasmin, S., Bukhari, M., & Kim, M. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e22879 · Cited by 6
3D regression neural network for the quantification of enlarged perivascular spaces in brain MRI
Dubost, F., Adams, H. H. H., Bortsova, G., Ikram, M. A., Niessen, W. J., Vernooij, M. W., & de Bruijne, M. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.10.008 · Cited by 7
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
Deep Convolution Neural Network Based Denoiser for Mammographic Images
Singh, G., Mittal, A., & Aggarwal, N. (2019) · Communications in computer and information science · doi:10.1007/978-981-13-9939-8_16 · Cited by 6
Osteoporosis Recognition in Rats under Low-Power Lens Based on Convexity Optimization Feature Fusion
Cai, J., He, W., Wang, L., Zhou, K., & Wu, T.-X. (2019) · Scientific Reports · doi:10.1038/s41598-019-47281-7 · Cited by 6
Lymph node detection method based on multisource transfer learning and convolutional neural network
Ma, Y., & Peng, Y. (2019) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22371 · Cited by 6
Localization and Diagnosis of Attention-Deficit/Hyperactivity Disorder
Wang, P., Zhao, X., Zhong, J., & Zhou, Y. Q. (2021) · Healthcare · doi:10.3390/healthcare9040372 · Cited by 6
Detection and Classification of Intracranial Brain Hemorrhage
Sharada, K. V., Prashanthi, V., & Kanakala, S. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-2919-8_41 · Cited by 6
Medical Image Analysis (MedIA) using Deep Learning
(2020) · International Journal of Innovative Technology and Exploring Engineering · doi:10.35940/ijitee.g1007.0597s20 · Cited by 6
IR Small Target Detection And Prediction With ANNs Trained Using ASSET
Sinn, Y. U., Hopkinson, K. M., Borghetti, B. J., & Steward, B. J. (2019) · IEEE Aerospace Conference (AERO) · doi:10.1109/aero.2019.8741671 · Cited by 6
Reinforcement Learning-based Automatic Diagnosis of Acute Appendicitis in Abdominal CTPreprint
Al, W. A., Yun, I. D., & Lee, K. J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.00617 · Cited by 9
A Novel Detection and Classification Framework for Diagnosing of Cerebral Microbleeds Using Transformer and Language
Chen, C., Zhao, L., Lang, Q., & Xu, Y. (2024) · Bioengineering · doi:10.3390/bioengineering11100993 · Cited by 5
Self-citation
Enhancing Alzheimer's Disease Diagnosis: A Hybrid Model of Transfer Learning and SVM for Improved Classification Accuracy
Hiremath, M. S., Biradar, R. C., Subhadarshini, L., Hiremath, S. S., Hiremath, J. S., & Sivanandan, S. K. (2024) · IEEE International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct61223.2024.10543710 · Cited by 6
Co-Learning Multimodality PET-CT Features via a Cascaded CNN-Transformer Network
Bi, L., Fu, X., Liu, Q., Song, S., Feng, D. D., FULHAM, M. J., & Kim, J. (2024) · IEEE Transactions on Radiation and Plasma Medical Sciences · doi:10.1109/trpms.2024.3417901 · Cited by 6
Role of artificial intelligence in cancer detection using protein p53: A Review
Patil, M. R., & Bihari, A. (2024) · Molecular Biology Reports · doi:10.1007/s11033-024-10051-4 · Cited by 6
PARADISE: Personalized and regional adaptation for HIE disease identification and segmentation
Bao, R., Weiss, R. J., Bates, S. V., Song, Y., he, S. T., Li, J., Bjørnerud, A., Hirschtick, R., Grant, P. E., & Ou, Y. (2025) · Medical Image Analysis · doi:10.1016/j.media.2024.103419 · Cited by 5
Dynamic colormap visualization integrated with Harris hawks optimization for enhanced lung CT segmentation and diagnostic precision
Drogham, O., Ryalat, M. H., Al-Najdawi, N. A., Alkhawaldeh, R. S., Shaqsi, J. A., & Al‐Betar, M. A. (2025) · Cluster Computing · doi:10.1007/s10586-025-05220-4 · Cited by 5
Bone Age Recognition Using Convolution Neural Network
王, 永. (2018) · Journal of Image and Signal Processing · doi:10.12677/jisp.2018.71001 · Cited by 5
A Review on Exploring the Deep Learning Concepts and Applications for Medical Diagnosis
Priya, P. S. S., Nandhinidevi, S., Dr.Thangamani, M., & Nallusamy, S. (2020) · International Journal of Engineering Trends and Technology · doi:10.14445/22315381/ijett-v68i10p211 · Cited by 5
Dynamic Logo Design System of Network Media Art Based on Convolutional Neural Network
Yang, Z. (2022) · Mobile Information Systems · doi:10.1155/2022/3247229 · Cited by 5
Retracted
A single-stage detector of cerebral microbleeds using 3D feature fused region proposal network (FFRP-Net)
Kim, J., Al‐masni, M. A., Lee, H., Choi, Y., & Kim, D. (2022) · IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) · doi:10.1109/aicas54282.2022.9869855 · Cited by 5
Applications of Deep Learning to Magnetic Resonance Imaging (MRI)
Meça, A. (2023) · International Conference on Computing, Electronics & Communications Engineering (iCCECE) · doi:10.1109/iccece59400.2023.10238598 · Cited by 6
Comparative Analysis of Convolutional Neural Networks for Brain Tumor Detection: A Study of VGG16, ResNet, Inception, and DenseNet Models
Kumar, C. M., & Sankar, J. S. (2024) · International Conference on Applied Artificial Intelligence and Computing (ICAAIC) · doi:10.1109/icaaic60222.2024.10575770 · Cited by 5
Deep Learning Techniques for Automated Dementia Diagnosis Using Neuroimaging Modalities: A Systematic Review
Özkan, D., KATAR, O., Ak, M., Al–antari, M. A., Ak, N. Y., Yıldırım, Ö., Mir, H., Tan, R., & Acharya, U. R. (2024) · IEEE Access · doi:10.1109/access.2024.3454709 · Cited by 7
Can the spherical gold standards be used as an alternative to painted gold standards for the computerized detection of lesions using voxel-based classification?
Nomura, Y., Hayashi, N., Hanaoka, S., Takenaga, T., Nemoto, M., Miki, S., Yoshikawa, T., & Abe, O. (2018) · Japanese Journal of Radiology · doi:10.1007/s11604-018-0784-6 · Cited by 5
Survey on Deep Learning Techniques for Medical Imaging Application Area
Arkoub, S. A., Hassani, A. H. E., Lauri, F., Hajjar, M., Daya, B., Hecquet, S., & Aubry, S. (2020) · Learning and analytics in intelligent systems · doi:10.1007/978-3-030-49724-8_7 · Cited by 5
Age Estimation from MR Images via 3D Convolutional Neural Network and Densely Connect
Qi, Q., Du, B., Zhuang, M., Huang, Y., & Ding, X. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-04239-4_37 · Cited by 5
Predicting the progression of mild cognitive impairment based on fine-grained and spatiotemporal features of MRI
Hu, Z., Wang, Y., Zhang, S., Li, Y., & Hou, W. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107895 · Cited by 5
A coronary artery CTA segmentation approach based on deep learning
Huang, C., & Yin, C. (2021) · Journal of X-Ray Science and Technology · doi:10.3233/xst-211063 · Cited by 4
Multi‐level learning based on 3D CT image integrated medical clinic information for accurate diagnosis of pulmonary nodules
Zhang, Y., Zhao, J., Wu, W. D., Qiang, Y., & Jia, L. (2022) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.6998 · Cited by 4
Deep learning IoT in medical and healthcare
Sharma, A., Sharma, A., Virmani, R., Kumar, G., Virmani, T., & Chitranshi, N. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-19413-9.00027-8 · Cited by 4
Towards Fine Whole-Slide Skeletal Muscle Image Segmentation through Deep Hierarchically Connected Networks
Cui, L., Feng, J. H., & Yang, L. (2019) · Journal of Healthcare Engineering · doi:10.1155/2019/5191630 · Cited by 4
Interpretation of 3D CNNs for Brain MRI Data Classification
Kan, M., Aliev, R., Rudenko, A., Drobyshev, N., Petrashen, N., Kondrateva, E., Sharaev, M., Bernstein, A., & Burnaev, E. V. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-71214-3_19 · Cited by 5
Detection of Brain Tumor Types Using Deep Learning
Roopa, Y. M., Kumar, G. N., Harsha, Y. V. S., & Aditya, P. S. (2022) · International Conference on Artificial Intelligence and Smart Energy (ICAIS) · doi:10.1109/icais53314.2022.9742731 · Cited by 5
Effective feature extraction for Cerebral Microbleed detection using Edge Emphasized Weber Maximum Directional Co-occurance Matrix
Stanley, B. F., & Franklin, S. W. (2022) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-022-04023-4 · Cited by 4
A scaling up approach: a research agenda for medical imaging analysis with applications in deep learning
Afriyie, Y., Weyori, B. A., & Opoku, A. A. (2023) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/0952813x.2023.2165721 · Cited by 4
Multimodal biomedical image retrieval and indexing system using handcrafted with deep convolution neural network features
Mansour, R. F. (2023) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-023-04575-z · Cited by 4
ResFCNET: A Skin Lesion Segmentation Method Based on a Deep Residual Fully Convolutional Neural Network
Mohammed, M. A., Bismark, O., Alornyo, S., Asante, M., & Essah, B. O. (2023) · IETI Transactions on Data Analysis and Forecasting (iTDAF) · doi:10.3991/itdaf.v1i1.35723 · Cited by 5
Multi-atlas subcortical segmentation: an orchestration of 3D fully convolutional network and generalized mixture function
Wu, J., He, S., & Zhou, S. (2023) · Machine Vision and Applications · doi:10.1007/s00138-023-01415-0 · Cited by 4
DNN-Based Assistant in Laparoscopic Computer-Aided Palpation
Fukuda, T., Tanaka, Y., Fujiwara, M., & Akihito, S. A. N. O. (2018) · Frontiers in Robotics and AI · doi:10.3389/frobt.2018.00071 · Cited by 4
Elements of Medical Image Processing
Azadi, T. E., Janney, S. S., & Chakravarty, S. (2019) · Springer eBooks · doi:10.1007/978-981-13-3705-5_20 · Cited by 4
False Positive Reduction in Lung Computed Tomography Images using Convolutional Neural NetworksPreprint
Polat, G., Halıcı, U., & Doğrusöz, Y. S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.01424 · Cited by 5
Neuroimaging in Epilepsy
Brinkmann, B. H., So, E. L., Watson, R. E., & Kotsenas, A. L. (2021) · Epilepsy · doi:10.1002/9781119431893.ch7 · Cited by 4
MixMicrobleedNet: segmentation of cerebral microbleeds using nnU-NetPreprint
Kuijf, H. J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.01389 · Cited by 4
A bibliometric analysis of cerebral small vessel disease
Yan, X., Zhang, Y., He, R., Chen, X., & Lin, M. (2024) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2024.1400844 · Cited by 3
WTAM-YOLO: a YOLOv11-based method for pulmonary nodule detection
Lan, Y., Zhang, Y., Xu, J., Zhang, Y., & Hu, T. (2026) · Scientific Reports · doi:10.1038/s41598-026-42029-6 · Cited by 3
A Comparison and Survey on Brain Tumour Detection Techniques Using MRI Images
Mahalaxmi, G., Tirupal, T., Shanawaz, S., Swarnakar, S., & Krishna, S. V. (2022) · Current Signal Transduction Therapy · doi:10.2174/1574362417666220601162839 · Cited by 3
Deep learning basics
Liao, H., Zhou, S., & Luo, J. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-824383-1.00009-5 · Cited by 3
Progressive Learning Based Knowledge Distillation for Low Resolution Cerebral Microbleed Segmentation
Xia, T., Zhang, R., Chen, Z., Xie, G., Wu, X., Lv, Z., & Guo, L. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447006 · Cited by 3
Defending Data Poisoning Attack through Watermarked Friendly Noise Luminosity Activated Dense Layered UNet for Classification of Lung Disease
Devi, M. S., & Saravanan, P. (2024) · IEEE Access · doi:10.1109/access.2024.3513485 · Cited by 4
Cerebral small vessel disease lesion segmentation methods: A systematic review
Phelps, J., Singh, M., McCreary, C. R., Dallaire‐Théroux, C., Stein, R. G., Potvin-Jutras, Z., Guan, D. X., Wu, J.-L. D., Metz, A., & Smith, E. E. (2025) · Cerebral Circulation - Cognition and Behavior · doi:10.1016/j.cccb.2025.100396 · Cited by 3
Understanding Deep Learning: Case Study Based Approach
Galphade, M., More, N., Nikam, V. B., Banerjee, B., & Kiwelekar, A. W. (2021) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-60265-9_9 · Cited by 3

Authors: Dou, Q., Chen, H., Yu, L., Zhao, L., Qin, J., Wang, D., Mok, V. C., Shi, L., & Heng, P.-A.
Affiliations: Chinese University of Hong Kong (HK)
Shenzhen University (CN)
ORCID: Dou, Qi
Chen, Hao
Yu, Lequan
Zhao, Lei
Qin, Jing
Wang, Defeng
Mok, Vincent CT
Shi, Lin
Heng, Pheng-Ann
Venue: IEEE Transactions on Medical Imaging
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2016-05
Volume: 35
Issue: 5
Pages: 1182-1195
ISSN: 0278-0062 (ISSN-L)
1558-254X
DOI: 10.1109/tmi.2016.2528129
OpenAlex: W2343172899
Cited by: 679
References: 51
Citations by year: 2026: 23 · 2025: 26 · 2024: 40 · 2023: 84 · 2022: 71 · 2021: 78 · 2020: 88 · 2019: 130 · 2018: 76 · 2017: 47 · 2016: 14
FWCI: 60.00
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations: IEEE Transactions on Medical Imaging (publishedVersion)
PubMed (publishedVersion)
Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Health Sciences › Medicine › Clinical Neurology › Intracerebral and Subarachnoid Hemorrhage Research
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Brain Tumor Detection and Classification
Keywords: cerebral microbleeds, automated detection, 3D convolutional neural networks, fully convolutional network, false positive reduction
MeSH terms: Brain, Cerebral Hemorrhage, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Biomarkers, Databases, Factual, Neural Networks, Computer, Imaging, Three-Dimensional

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks