Ze Jin
Orcid: 0000-0003-3379-5113
According to our database1,
Ze Jin
authored at least 28 papers
between 2013 and 2024.
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Bibliography
2024
SMFE-Net: a saliency multi-feature extraction framework for VHR remote sensing image classification.
Multim. Tools Appl., January, 2024
2023
FusionFlow: Accelerating Data Preparation for Machine Learning with Hybrid CPU-GPU Processing.
Proc. VLDB Endow., December, 2023
EFCOMFF-Net: A Multiscale Feature Fusion Architecture With Enhanced Feature Correlation for Remote Sensing Image Scene Classification.
IEEE Trans. Geosci. Remote. Sens., 2023
CoRR, 2023
Explaining Massive-Training Artificial Neural Networks in Medical Image Analysis Task Through Visualizing Functions Within the Models.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023
Radiation Dose Reduction in Digital Breast Tomosynthesis by MTANN with Multi-scale Kernels.
Proceedings of the 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2023
Proceedings of the 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Network, 2023
ANDetect: A Third-party Ad Network Libraries Detection Framework for Android Applications.
Proceedings of the Annual Computer Security Applications Conference, 2023
2022
ConvPatchTrans: A script identification network with global and local semantics deeply integrated.
Eng. Appl. Artif. Intell., 2022
FedAL: An Federated Active Learning Framework for Efficient Labeling in Skin Lesion Analysis.
Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, 2022
Proceedings of the Machine Learning in Medical Imaging - 13th International Workshop, 2022
P-Verifier: Understanding and Mitigating Security Risks in Cloud-based IoT Access Policies.
Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, 2022
2021
Automatic expression recognition of face image sequence based on key-frame generation and differential emotion feature.
Appl. Soft Comput., 2021
Massive-Training Artificial Neural Network (Mtann) With Special Kernel For Artifact Reduction In Fast-Acquisition Mri Of The Knee.
Proceedings of the 18th IEEE International Symposium on Biomedical Imaging, 2021
Semantic Segmentation of Liver Tumor in Contrast-enhanced Hepatic CT by Using Deep Learning with Hessian-based Enhancer with Small Training Dataset Size.
Proceedings of the 18th IEEE International Symposium on Biomedical Imaging, 2021
2020
Obtaining the potential number of object models/atlases needed in medical image analysis.
Proceedings of the Medical Imaging 2020: Image-Guided Procedures, 2020
Proceedings of the 36th IEEE International Conference on Data Engineering, 2020
2019
Stat. Anal. Data Min., 2019
How many models/atlases are needed as priors for capturing anatomic population variations?
Medical Image Anal., 2019
Proceedings of the 16th USENIX Symposium on Networked Systems Design and Implementation, 2019
Obtaining the potential number of models/atlases needed for capturing anatomic variations in population images.
Proceedings of the Medical Imaging 2019: Image Processing, 2019
Proceedings of the 18th IEEE International Conference On Machine Learning And Applications, 2019
Development of Deep-learning Segmentation for Breast Cancer in MR Images based on Neural Network Convolution.
Proceedings of the ICCPR '19: 8th International Conference on Computing and Pattern Recognition, 2019
2018
Generalizing distance covariance to measure and test multivariate mutual dependence via complete and incomplete V-statistics.
J. Multivar. Anal., 2018
Testing for Conditional Mean Independence with Covariates through Martingale Difference Divergence.
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018
2016
Proceedings of the 2016 IEEE International Conference on Big Data (IEEE BigData 2016), 2016
2015
Proceedings of the Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques, 2015
2013
Automated method for extraction of lung tumors using a machine learning classifier with knowledge of radiation oncologists on data sets of planning CT and FDG-PET/CT images.
Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2013