Michael Z. Liu

1.4k total citations
44 papers, 991 citations indexed

About

Michael Z. Liu is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, Michael Z. Liu has authored 44 papers receiving a total of 991 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Radiology, Nuclear Medicine and Imaging, 14 papers in Artificial Intelligence and 14 papers in Electrical and Electronic Engineering. Recurrent topics in Michael Z. Liu's work include Radiomics and Machine Learning in Medical Imaging (14 papers), AI in cancer detection (14 papers) and MRI in cancer diagnosis (12 papers). Michael Z. Liu is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (14 papers), AI in cancer detection (14 papers) and MRI in cancer diagnosis (12 papers). Michael Z. Liu collaborates with scholars based in United States, Australia and United Kingdom. Michael Z. Liu's co-authors include Sachin Jambawalikar, Richard Ha, Simukayi Mutasa, Peter Chang, Jenika Karcich, Luis F. Ochoa, Eduardo Pascual Van Sant, Ralph Wynn, Jack Grinband and Mary Sun and has published in prestigious journals such as PLoS ONE, IEEE Transactions on Power Systems and Chemistry - A European Journal.

In The Last Decade

Michael Z. Liu

40 papers receiving 978 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Michael Z. Liu United States	19	640	427	151	151	104	44	991
Yanyan Yu China	14	580 0.9×	236 0.6×	113 0.7×	57 0.4×	72 0.7×	60	893
Xiaohong Ma China	16	536 0.8×	211 0.5×	133 0.9×	73 0.5×	47 0.5×	57	948
Kentaro Kobayashi Japan	18	306 0.5×	44 0.1×	106 0.7×	373 2.5×	106 1.0×	144	1.1k
Ying Liang China	13	298 0.5×	200 0.5×	152 1.0×	58 0.4×	49 0.5×	66	673
Matteo Interlenghi Italy	15	577 0.9×	280 0.7×	171 1.1×	11 0.1×	27 0.3×	29	986
Wai Yee Chan Malaysia	16	416 0.7×	216 0.5×	392 2.6×	10 0.1×	123 1.2×	40	1.0k
Yinsheng Chen China	18	1.1k 1.7×	208 0.5×	291 1.9×	70 0.5×	130 1.3×	53	1.6k
Avishek Chatterjee Netherlands	17	815 1.3×	340 0.8×	283 1.9×	14 0.1×	31 0.3×	38	1.2k
Yulong Yan United States	19	547 0.9×	81 0.2×	546 3.6×	137 0.9×	10 0.1×	88	1.1k
Kazuma Kobayashi Japan	14	241 0.4×	193 0.5×	65 0.4×	11 0.1×	80 0.8×	32	638

Countries citing papers authored by Michael Z. Liu

Since Specialization

Citations

This map shows the geographic impact of Michael Z. Liu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Michael Z. Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Z. Liu more than expected).

Fields of papers citing papers by Michael Z. Liu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael Z. Liu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Michael Z. Liu. The network helps show where Michael Z. Liu may publish in the future.

Co-authorship network of co-authors of Michael Z. Liu

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Z. Liu. A scholar is included among the top collaborators of Michael Z. Liu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Michael Z. Liu. Michael Z. Liu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Liu, Michael Z., Hien Nguyen, Jinyu Lu, et al.. (2025). A deep learning framework to stratify Nottingham histologic grade 2 breast tumors based on dynamic contrast-enhanced MRI. European Radiology.

Zhu, Jing, et al.. (2025). MV-LV Rule-Based Control of EV Charging Using Limited Network and Monitoring Data. IEEE Transactions on Power Systems. 40(5). 3695–3707.

Liu, Michael Z., et al.. (2024). Increasing PV hosting capacity with phase rebalancing in LV networks: A network-agnostic rule-based approach. Sustainable Energy Grids and Networks. 41. 101615–101615.

Huang, Alice, et al.. (2023). Emerging uses of artificial intelligence in breast and axillary ultrasound. Clinical Imaging. 100. 64–68. 9 indexed citations

Liu, Michael Z., et al.. (2022). Planning of Future-Proof Residential LV Networks. 1–5.

Keenan, Kathryn E., Zydrunas Gimbutas, Andrew Dienstfrey, et al.. (2021). Multi-site, multi-platform comparison of MRI T1 measurement using the system phantom. PLoS ONE. 16(6). e0252966–e0252966. 28 indexed citations

Liu, Michael Z., et al.. (2021). Grid and Market Services From the Edge: Using Operating Envelopes to Unlock Network-Aware Bottom-Up Flexibility. IEEE Power and Energy Magazine. 19(4). 52–62. 50 indexed citations

Liu, Yulin, R. Vanguri, Michael Z. Liu, et al.. (2021). 3D Isotropic Super-resolution Prostate MRI Using Generative Adversarial Networks and Unpaired Multiplane Slices. Journal of Digital Imaging. 34(5). 1199–1208. 9 indexed citations

Procopiou, Andreas T., et al.. (2020). Smart meter-driven estimation of PV hosting capacity. CIRED - Open Access Proceedings Journal. 2020(1). 128–131. 10 indexed citations

10.

Liu, Michael Z., R. Vanguri, Simukayi Mutasa, et al.. (2020). Channel width optimized neural networks for liver and vessel segmentation in liver iron quantification. Computers in Biology and Medicine. 122. 103798–103798. 17 indexed citations

11.

Liu, Michael Z., et al.. (2019). Implementable Three-Phase OPF Formulations for MV-LV Distribution Networks: MILP and MIQCP. 1–6. 17 indexed citations

12.

Malyarenko, Dariya, Scott D. Swanson, Amaresha Shridhar Konar, et al.. (2019). Multicenter Repeatability Study of a Novel Quantitative Diffusion Kurtosis Imaging Phantom. Tomography. 5(1). 36–43. 12 indexed citations

13.

Cattell, Renee, Haifang Li, R. Vanguri, et al.. (2019). Convolutional Neural Network Detection of Axillary Lymph Node Metastasis Using Standard Clinical Breast MRI. Clinical Breast Cancer. 20(3). e301–e308. 46 indexed citations

14.

Wong, Tony T., et al.. (2019). How Many Radiographs Does It Take to Screen for Femoral Cam Morphology?: A Noninferiority Study. Current Problems in Diagnostic Radiology. 50(1). 48–53. 1 indexed citations

15.

Liu, Michael Z. & Luis F. Ochoa. (2019). Hardware-In-the-Loop Demonstration of Advanced Control Schemes for Active Distribution Networks. 1–6. 4 indexed citations

16.

Mutasa, Simukayi, Peter Chang, Eduardo Pascual Van Sant, et al.. (2019). Potential Role of Convolutional Neural Network Based Algorithm in Patient Selection for DCIS Observation Trials Using a Mammogram Dataset. Academic Radiology. 27(6). 774–779. 11 indexed citations

17.

Ha, Richard, Peter Chang, Simukayi Mutasa, et al.. (2018). Convolutional Neural Network Using a Breast MRI Tumor Dataset Can Predict Oncotype Dx Recurrence Score. Journal of Magnetic Resonance Imaging. 49(2). 518–524. 56 indexed citations

18.

Ha, Richard, Peter Chang, Jenika Karcich, et al.. (2018). Predicting Post Neoadjuvant Axillary Response Using a Novel Convolutional Neural Network Algorithm. Annals of Surgical Oncology. 25(10). 3037–3043. 26 indexed citations

19.

Jambawalikar, Sachin, Michael Z. Liu, & Gul Moonis. (2018). Advanced MR Imaging of the Temporal Bone. Neuroimaging Clinics of North America. 29(1). 197–202. 5 indexed citations

20.

Ha, Richard, Peter Chang, Jenika Karcich, et al.. (2018). Convolutional Neural Network Based Breast Cancer Risk Stratification Using a Mammographic Dataset. Academic Radiology. 26(4). 544–549. 44 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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