Yu‐Ching Lee

1.0k total citations
39 papers, 759 citations indexed

About

Yu‐Ching Lee is a scholar working on Molecular Biology, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Yu‐Ching Lee has authored 39 papers receiving a total of 759 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 13 papers in Artificial Intelligence and 12 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Yu‐Ching Lee's work include AI in cancer detection (13 papers), Monoclonal and Polyclonal Antibodies Research (7 papers) and Cell Image Analysis Techniques (5 papers). Yu‐Ching Lee is often cited by papers focused on AI in cancer detection (13 papers), Monoclonal and Polyclonal Antibodies Research (7 papers) and Cell Image Analysis Techniques (5 papers). Yu‐Ching Lee collaborates with scholars based in Taiwan, Canada and Japan. Yu‐Ching Lee's co-authors include Ching‐Wei Wang, Tai‐Kuang Chao, Yi‐Jia Lin, Chih‐Hung Wang, Cheng‐Chang Chang, Gwo‐Ching Gong, Chih‐hao Hsieh, Akash R. Sastri, Keng‐Chang Tsai and Carmen García‐Comas and has published in prestigious journals such as Bioinformatics, Applied and Environmental Microbiology and Journal of Agricultural and Food Chemistry.

In The Last Decade

Yu‐Ching Lee

39 papers receiving 738 citations

Peers

Yu‐Ching Lee

RNMI

CVPR

BIOPH

Harald Vöhringer Germany

Neil H. Anderson United Kingdom

Jyrki Selinummi Finland

Yihao Liu China

Shiwei Wang China

Kourosh Zarringhalam United States

Guylaine Collewet France

Sihua Peng China

Duolin Wang United States

Changlu Guo China

Harald Vöhringer Germany

Yu‐Ching Lee

223 ×0.7

198 ×0.8

RNMI

724 ×5.7

38 ×0.3

CVPR

64 ×0.7

BIOPH

Citations per year, relative to Yu‐Ching Lee Yu‐Ching Lee (= 1×) peers Harald Vöhringer

Countries citing papers authored by Yu‐Ching Lee

Since Specialization

Citations

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

Fields of papers citing papers by Yu‐Ching Lee

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yu‐Ching Lee. 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 Yu‐Ching Lee. The network helps show where Yu‐Ching Lee may publish in the future.

Co-authorship network of co-authors of Yu‐Ching Lee

This figure shows the co-authorship network connecting the top 25 collaborators of Yu‐Ching Lee. A scholar is included among the top collaborators of Yu‐Ching Lee 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 Yu‐Ching Lee. Yu‐Ching Lee 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

Wang, Ching‐Wei, Yu‐Ching Lee, Yu‐Chi Wang, et al.. (2025). Interpretable multi-stage attention network to predict cancer subtype, microsatellite instability, TP53 mutation and TMB of endometrial and colorectal cancer. Computerized Medical Imaging and Graphics. 121. 102499–102499. 1 indexed citations

Liu, Ching‐Hsuan, Sy‐Jye Leu, Yu‐Ching Lee, et al.. (2024). Production and characterization of single-chain variable fragment antibodies targeting the breast cancer tumor marker nectin-4. Frontiers in Immunology. 14. 1292019–1292019. 2 indexed citations

Wang, Ching‐Wei, Yu‐Ching Lee, Yi‐Jia Lin, et al.. (2023). Deep Learning Can Predict Bevacizumab Therapeutic Effect and Microsatellite Instability Directly from Histology in Epithelial Ovarian Cancer. Laboratory Investigation. 103(11). 100247–100247. 14 indexed citations

Wang, Ching‐Wei, et al.. (2023). Detection of ERBB2 and CEN17 signals in fluorescent in situ hybridization and dual in situ hybridization for guiding breast cancer HER2 target therapy. Artificial Intelligence in Medicine. 141. 102568–102568. 10 indexed citations

Wang, Ching‐Wei, et al.. (2023). Annotation-Free Deep Learning-Based Prediction of Thyroid Molecular Cancer Biomarker BRAF (V600E) from Cytological Slides. International Journal of Molecular Sciences. 24(3). 2521–2521. 22 indexed citations

Lee, Yu‐Ching, Tsai‐Yu Lin, Tien‐Sheng Tseng, et al.. (2023). Development of anti-aflatoxin B1 nanobodies from a novel mutagenesis-derived synthetic library for traditional Chinese medicine and foods safety testing. Journal of Biological Engineering. 17(1). 30–30. 10 indexed citations

Tsai, Keng‐Chang, et al.. (2023). The Blockade of Mitogen-Activated Protein Kinase 14 Activation by Marine Natural Product Crassolide Triggers ICD in Tumor Cells and Stimulates Anti-Tumor Immunity. Marine Drugs. 21(4). 225–225. 4 indexed citations

Wang, Ching‐Wei, et al.. (2023). Interpretable attention-based deep learning ensemble for personalized ovarian cancer treatment without manual annotations. Computerized Medical Imaging and Graphics. 107. 102233–102233. 19 indexed citations

Wang, Ching‐Wei, et al.. (2023). CW-NET for multitype cell detection and classification in bone marrow examination and mitotic figure examination. Bioinformatics. 39(6). 11 indexed citations

10.

Wang, Ching‐Wei, Yu‐Ching Lee, Cheng‐Chang Chang, et al.. (2022). A Weakly Supervised Deep Learning Method for Guiding Ovarian Cancer Treatment and Identifying an Effective Biomarker. Cancers. 14(7). 1651–1651. 36 indexed citations

11.

Wang, Ching‐Wei, Cheng‐Chang Chang, Yi‐Jia Lin, et al.. (2022). Histopathological whole slide image dataset for classification of treatment effectiveness to ovarian cancer. Scientific Data. 9(1). 25–25. 17 indexed citations

12.

Wang, Ching‐Wei, Cheng‐Chang Chang, Yu‐Ching Lee, et al.. (2022). Weakly supervised deep learning for prediction of treatment effectiveness on ovarian cancer from histopathology images. Computerized Medical Imaging and Graphics. 99. 102093–102093. 44 indexed citations

13.

Wang, Ching‐Wei, et al.. (2021). Deep learning for bone marrow cell detection and classification on whole-slide images. Medical Image Analysis. 75. 102270–102270. 83 indexed citations

14.

Lee, Yu‐Ching, et al.. (2021). A real-time camera-based adaptive breathing monitoring system. Medical & Biological Engineering & Computing. 59(6). 1285–1298. 16 indexed citations

15.

Wang, Ching‐Wei, Yi‐Jia Lin, Cheng‐Chang Chang, et al.. (2021). Artificial intelligence-assisted fast screening cervical high grade squamous intraepithelial lesion and squamous cell carcinoma diagnosis and treatment planning. Scientific Reports. 11(1). 16244–16244. 67 indexed citations

16.

Lee, Yu‐Ching, et al.. (2021). Fully Automatic Registration Methods for Chest X-Ray Images. Journal of Medical and Biological Engineering. 41(6). 826–843. 1 indexed citations

17.

Wang, Ching‐Wei, et al.. (2017). Ensemble Neuron Tracer for 3D Neuron Reconstruction. Neuroinformatics. 15(2). 185–198. 30 indexed citations

18.

Wong, Yung-Hao, et al.. (2016). A New Era for Cancer Target Therapies: Applying Systems Biology and Computer-Aided Drug Design to Cancer Therapies. Current Pharmaceutical Biotechnology. 17(14). 1246–1267. 3 indexed citations

19.

Leu, Sy‐Jye, Yu‐Ching Lee, Neng‐Yao Shih, et al.. (2010). Generation and characterization of anti-α-enolase single-chain antibodies in chicken. Veterinary Immunology and Immunopathology. 137(3-4). 251–260. 11 indexed citations

20.

Lee, Yu‐Ching, Sy-Jye C. Leu, Chaur‐Jong Hu, et al.. (2007). Chicken single-chain variable fragments against the SARS-CoV spike protein. Journal of Virological Methods. 146(1-2). 104–111. 12 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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