Meiyan Liang, PhD, is an accomplished researcher in the field of Instrument Science and Technology, with a focus on Deep Learning and Medical Image Processing. Currently affiliated with the School of Physics and Electronic Engineering at Shanxi University in China, Dr. Liang completed her PhD at the Opto-Electronic College, Beijing Institute of Technology. She has made significant contributions to the development of innovative technologies for the identification and classification of various medical conditions, particularly in cancer diagnosis. Her work spans both theoretical and experimental domains, with a particular emphasis on leveraging neural networks and terahertz imaging techniques. Dr. Liang’s expertise is recognized through numerous awards, patents, and a prolific publication record in prestigious journals.
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Dr. Liang’s research output is not only extensive but also impactful, as evidenced by her author metrics. She has consistently published in high-impact journals, demonstrating the significance of her work within the scientific community. Additionally, Dr. Liang’s patents highlight her innovative approach to problem-solving and technology development.
- Citations: 138 citations across 136 documents
- Documents: Authored 25 documents
- h-index: 5
Education
Dr. Meiyan Liang obtained her PhD in Instrument Science and Technology from the Opto-Electronic College at Beijing Institute of Technology. Her doctoral research focused on the application of deep learning methodologies in medical image processing, particularly for cancer diagnosis.
Research Focus
Dr. Liang’s research primarily centers around two main areas: Deep Learning and Medical Image Processing. Within these domains, she specializes in utilizing neural networks for the interpretation and analysis of medical images, with a particular emphasis on cancer detection and classification. Her work also involves the integration of advanced imaging techniques, such as terahertz imaging, to develop novel diagnostic tools.
Professional Journey
Following her doctoral studies, Dr. Liang embarked on a professional journey that has seen her become an esteemed researcher in the field of medical imaging. She has held positions at various academic institutions, including her current role at Shanxi University. Throughout her career, Dr. Liang has secured research funding, published extensively, and obtained several patents for her innovative contributions to the field.
Honors & Awards
Dr. Liang’s outstanding contributions to her field have been recognized through numerous honors and awards. Notable accolades include being awarded the “Sanjin talent” by the government of Shanxi Province and receiving the “China Instrument & Control Society Scholarship” from the Chinese instrumentation society.
Research Timeline
Dr. Liang’s research timeline showcases her progression as a researcher and the evolution of her research interests. Starting from her doctoral studies, she has continued to expand her expertise and contribute to advancements in medical imaging technology. Her research timeline reflects a commitment to excellence and a dedication to addressing critical challenges in healthcare through innovative research.
Publications Noted & Contributions
Dr. Liang has made significant contributions to the academic community through her prolific publication record. Her research findings have been published in prestigious journals such as the IEEE Journal of Biomedical and Health Informatics, Computer Methods and Programs in Biomedicine, and The Visual Computer. These publications cover a wide range of topics, including interpretable inference, whole-slide image prediction, and pathology image restoration.
Title: Interpretable Inference and Classification of Tissue Types in Histological Colorectal Cancer Slides Based on Ensembles Adaptive Boosting Prototype Tree
- Authors: Liang, M., Wang, R., Liang, J., Zhang, T., Zhang, C.
- Journal: IEEE Journal of Biomedical and Health Informatics, 2023, 27(12), pp. 6006–6017
- Abstract: This paper presents a method for interpretable inference and classification of tissue types in histological colorectal cancer slides using ensembles adaptive boosting prototype tree.
Title: Multi-scale self-attention generative adversarial network for pathology image restoration
- Authors: Liang, M., Zhang, Q., Wang, G., Liu, H., Zhang, C.
- Journal: Visual Computer, 2023, 39(9), pp. 4305–4321
- Abstract: This paper introduces a multi-scale self-attention generative adversarial network for pathology image restoration.
- Citations: 1
Title: Interpretable classification of pathology whole-slide images using attention based context-aware graph convolutional neural network
- Authors: Liang, M., Chen, Q., Li, B., Jiang, X., Zhang, C.
- Journal: Computer Methods and Programs in Biomedicine, 2023, 229, 107268
- Abstract: This paper proposes an interpretable classification method for pathology whole-slide images using an attention-based context-aware graph convolutional neural network.
- Citations: 6
Title: A novel strategy regarding geometric product for liquids discrimination based on THz reflection spectroscopy
- Authors: Liu, H., Liu, X., Zhang, Z., Liang, M., Zhang, C.
- Journal: Spectrochimica Acta – Part A: Molecular and Biomolecular Spectroscopy, 2022, 274, 121104
- Abstract: This paper proposes a novel strategy for liquids discrimination based on THz reflection spectroscopy using the geometric product.
- Citations: 1
Title: THz ISAR imaging using GPU-accelerated phase compensated back projection algorithm
- Authors: Liang, M.-Y., Ren, Z.-Y., Li, G.-H., Zhang, C.-L., Fathy, A.E.
- Journal: Hongwai Yu Haomibo Xuebao/Journal of Infrared and Millimeter Waves, 2022, 41(2), pp. 448–456
- Abstract: This paper presents THz ISAR imaging using a GPU-accelerated phase-compensated back projection algorithm.
- Citations: 3
