Ling Ma
Orcid: 0000-0002-4352-5697Affiliations:
- University of Texas at Dallas, Richardson, TX, USA
- Tianjin University, State Key Lab of Precision Measuring Technology and Instruments, China (PhD 2020)
According to our database1,
Ling Ma
authored at least 29 papers
between 2016 and 2024.
Collaborative distances:
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Bibliography
2024
Biomed. Signal Process. Control., January, 2024
2023
Proceedings of the Medical Imaging 2023: Image-Guided Procedures, 2023
Lung nodule false positive reduction using a central attention convolutional neural network on imbalanced data.
Proceedings of the Medical Imaging 2023: Image-Guided Procedures, 2023
Proceedings of the Medical Imaging 2023: Digital and Computational Pathology, 2023
2022
Semi-automated three-dimensional segmentation for cardiac CT images using deep learning and randomly distributed points.
Proceedings of the Medical Imaging 2022: Image-Guided Procedures, 2022
Automatic detection of head and neck squamous cell carcinoma on pathologic slides using polarized hyperspectral imaging and deep learning.
Proceedings of the Medical Imaging 2022: Digital and Computational Pathology, 2022
Thyroid carcinoma detection on whole histologic slides using hyperspectral imaging and deep learning.
Proceedings of the Medical Imaging 2022: Digital and Computational Pathology, 2022
Proceedings of the Medical Imaging 2022: Digital and Computational Pathology, 2022
2021
Proceedings of the Medical Imaging 2021: Image-Guided Procedures, 2021
Automatic segmentation of the prostate on MR images based on anatomy and deep learning.
Proceedings of the Medical Imaging 2021: Image-Guided Procedures, 2021
Pixel-level tumor margin assessment of surgical specimen with hyperspectral imaging and deep learning classification.
Proceedings of the Medical Imaging 2021: Image-Guided Procedures, 2021
Automatic detection of head and neck squamous cell carcinoma on pathologic slides using polarized hyperspectral imaging and machine learning.
Proceedings of the Medical Imaging 2021: Digital Pathology, Online, February 15-19, 2021, 2021
Hyperspectral microscopic imaging for the detection of head and neck squamous cell carcinoma on histologic slides.
Proceedings of the Medical Imaging 2021: Digital Pathology, Online, February 15-19, 2021, 2021
2020
A multi-level similarity measure for the retrieval of the common CT imaging signs of lung diseases.
Medical Biol. Eng. Comput., 2020
Hyperspectral microscopic imaging for automatic detection of head and neck squamous cell carcinoma using histologic image and machine learning.
Proceedings of the Medical Imaging 2020: Digital Pathology, 2020
In vivo cancer detection in animal model using hyperspectral image classification with wavelet feature extraction.
Proceedings of the Medical Imaging 2020: Biomedical Applications in Molecular, 2020
2019
Adaptive deep learning for head and neck cancer detection using hyperspectral imaging.
Vis. Comput. Ind. Biomed. Art, 2019
Towed Array Shape Estimation Based on Single or Double Near-Field Calibrating Sources.
Circuits Syst. Signal Process., 2019
2018
Signal Process. Image Commun., 2018
Pattern Recognit., 2018
A semiautomatic algorithm for three-dimensional segmentation of the prostate on CT images using shape and local texture characteristics.
Proceedings of the Medical Imaging 2018: Image-Guided Procedures, 2018
Convolutional neural networks for the detection of diseased hearts using CT images and left atrium patches.
Proceedings of the Medical Imaging 2018: Computer-Aided Diagnosis, 2018
Proceedings of the Medical Imaging 2018: Biomedical Applications in Molecular, 2018
2017
Robust method for interest region description based on local intensity binary pattern.
J. Electronic Imaging, 2017
Automatic segmentation of the prostate on CT images using deep learning and multi-atlas fusion.
Proceedings of the Medical Imaging 2017: Image Processing, 2017
Deep learning based classification for head and neck cancer detection with hyperspectral imaging in an animal model.
Proceedings of the Medical Imaging 2017: Biomedical Applications in Molecular, 2017
2016
Combining population and patient-specific characteristics for prostate segmentation on 3D CT images.
Proceedings of the Medical Imaging 2016: Image Processing, 2016
Proceedings of the Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, San Diego, California, United States, 27 February, 2016