Miao Wu

441 total citations
13 papers, 323 citations indexed

About

Miao Wu is a scholar working on Infectious Diseases, Surgery and Pathology and Forensic Medicine. According to data from OpenAlex, Miao Wu has authored 13 papers receiving a total of 323 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Infectious Diseases, 3 papers in Surgery and 3 papers in Pathology and Forensic Medicine. Recurrent topics in Miao Wu's work include Parasitic infections in humans and animals (3 papers), Amoebic Infections and Treatments (3 papers) and AI in cancer detection (3 papers). Miao Wu is often cited by papers focused on Parasitic infections in humans and animals (3 papers), Amoebic Infections and Treatments (3 papers) and AI in cancer detection (3 papers). Miao Wu collaborates with scholars based in China and New Zealand. Miao Wu's co-authors include Huiqiang Liu, Qian Liu, Yi Yin, Qian Liu, Xiaohui Wang, Fude Shang, Meifang Dong, Wangjun Yuan, Yuanji Han and Liya Cao and has published in prestigious journals such as Scientific Reports, Plant Molecular Biology and American Journal of Tropical Medicine and Hygiene.

In The Last Decade

Miao Wu

12 papers receiving 315 citations

Peers

Miao Wu

AI

MB

RNMI

BIOCH

CVPR

Chaoyi Wu China

Myungeun Lee South Korea

Mohammad Asadur Rahman Bangladesh

Chenghua Liu China

Shihao Wang China

Yingping Li China

Kokeb Dese Ethiopia

Ananda Mohan Mondal United States

Dinanath Sulakhe United States

Chaoyi Wu China

Miao Wu

200 ×1.3

AI

83 ×0.8

MB

108 ×1.1

RNMI

10 ×0.2

BIOCH

65 ×1.5

CVPR

Citations per year, relative to Miao Wu Miao Wu (= 1×) peers Chaoyi Wu

Countries citing papers authored by Miao Wu

Since Specialization

Citations

This map shows the geographic impact of Miao Wu'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 Miao Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Miao Wu more than expected).

Fields of papers citing papers by Miao Wu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Miao Wu. 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 Miao Wu. The network helps show where Miao Wu may publish in the future.

Co-authorship network of co-authors of Miao Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Miao Wu. A scholar is included among the top collaborators of Miao Wu 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 Miao Wu. Miao Wu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

13 of 13 papers shown

1.

Wu, Miao, et al.. (2025). Computer-aided diagnosis of hepatic cystic echinococcosis based on deep transfer learning features from ultrasound images. Scientific Reports. 15(1). 607–607. 1 indexed citations

2.

Wu, Miao, et al.. (2025). MDMU-Net: 3D multi-dimensional decoupled multi-scale U-Net for pancreatic cancer segmentation. PeerJ Computer Science. 11. e3059–e3059.

3.

Song, Tao, et al.. (2024). Clinical Application of Artificial Intelligence in the Ultrasound Classification of Hepatic Cystic Echinococcosis. American Journal of Tropical Medicine and Hygiene. 111(1). 93–101. 2 indexed citations

4.

Wu, Miao, et al.. (2023). A comparative evaluation of child health care in China using multicriteria decision analysis methods. BMC Health Services Research. 23(1). 1217–1217. 6 indexed citations

5.

Li, Zhonghua, Jing Ji, Ben Ma, et al.. (2022). Temporal Grading Index of Functional Network Topology Predicts Pain Perception of Patients With Chronic Back Pain. Frontiers in Neurology. 13. 899254–899254. 7 indexed citations

6.

Wu, Miao, et al.. (2022). Multi-Classification of Brain Tumors on Magnetic Resonance ImagesUsing an Ensemble of Pre-Trained Convolutional Neural Networks. Current Medical Imaging Formerly Current Medical Imaging Reviews. 19(1). 65–76. 3 indexed citations

7.

Wu, Miao, Xiaoxia Shen, Can Lai, et al.. (2021). Detecting neonatal acute bilirubin encephalopathy based on T1-weighted MRI images and learning-based approaches. BMC Medical Imaging. 21(1). 103–103. 10 indexed citations

8.

Wu, Miao, et al.. (2021). Detecting acute bilirubin encephalopathy in neonates based on multimodal MRI with deep learning. Pediatric Research. 91(5). 1168–1175. 11 indexed citations

9.

Wu, Miao, et al.. (2021). Automatic Classification of Hepatic Cystic Echinococcosis Using Ultrasound Images and Deep Learning. Journal of Ultrasound in Medicine. 41(1). 163–174. 22 indexed citations

10.

Zhou, Yaxing, et al.. (2019). The Silencing of SFRP2 Expression in ESCC Is Due to Methylation of the Gene Promoter. Technology in Cancer Research & Treatment. 18. 1078145625–1078145625. 2 indexed citations

11.

Wu, Miao, et al.. (2018). Automatic classification of ovarian cancer types from cytological images using deep convolutional neural networks. Bioscience Reports. 38(3). 86 indexed citations

12.

Wu, Miao, et al.. (2018). Automatic classification of cervical cancer from cytological images by using convolutional neural network. Bioscience Reports. 38(6). 85 indexed citations

13.

Han, Yuanji, Miao Wu, Liya Cao, et al.. (2016). Characterization of OfWRKY3, a transcription factor that positively regulates the carotenoid cleavage dioxygenase gene OfCCD4 in Osmanthus fragrans. Plant Molecular Biology. 91(4-5). 485–496. 88 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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