Ju Han

669 citations
23 papers · 482 · h-index 13

Impact in

Papers in

Ju Han

21 papers receiving 471 citations

Peers

Ju Han
Comparison fields: 5 of 77
  • Biophysics 150
  • Computer Vision and Pattern Recognition 220
  • Artificial Intelligence 229
  • Media Technology 45
  • Radiology, Nuclear Medicine and Imaging 86
Replace Wiem Lassoued with:
Wiem Lassoued United States
Lauri Goodell United States
Isabel Vallcorba Spain
Daniel Heim Germany
Sharath R. Cholleti United States
Xiaojun Guan China
Tokiya Abe Japan
Peter D. Caie United Kingdom
Henrik Failmezger Germany
Mikhail Teverovskiy United States
Ju Han relative to Wiem Lassoued United States Wiem Lassoued's profile →
Citations per field
00.5×1.5×
Wiem Lassoued · 1×
Citations per year

Countries citing papers authored by Ju Han

Since Specialization
Citations

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

Fields of papers citing papers by Ju Han

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Ju Han, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ju Han Line = papers co-authored together Ju Han links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 23 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2007123
2 201273
3 201544
4 201141
5 201338
6 201222
7 201121
8 201617
9 201617
10 201617
11 200814
12 201214
13 201613
14 20128
15 20166
16 20124
17 20123
18 20083
19 20092
20 20101

About Ju Han

Ju Han is a scholar working on Biophysics, Molecular Biology, Artificial Intelligence, Computer Vision and Pattern Recognition and Oncology, having authored 23 papers that have together received 482 indexed citations. Recurring topics across this work include Cell Image Analysis Techniques (12 papers), AI in cancer detection (8 papers), Gene expression and cancer classification (7 papers), Radiomics and Machine Learning in Medical Imaging (4 papers), Image Processing Techniques and Applications (3 papers), Cancer Cells and Metastasis (3 papers), Medical Image Segmentation Techniques (3 papers) and Cytokine Signaling Pathways and Interactions (2 papers). The work is most often cited by research in Biophysics (150 citations), Computer Vision and Pattern Recognition (220 citations), Artificial Intelligence (229 citations), Media Technology (45 citations) and Radiology, Nuclear Medicine and Imaging (86 citations). Ju Han has collaborated with scholars based in United States, China and Australia. Frequent co-authors include Hang Chang, Bahram Parvin, Joe W. Gray, Paul T. Spellman, Mary Helen Barcellos‐Hoff, Qing Yang, Björn Rydberg, Alexander D. Borowsky, Bahram Parvin and Gerald Fontenay. Their work appears in journals such as PLoS ONE, Scientific Reports, IEEE Transactions on Image Processing, BMC Bioinformatics and IEEE/ACM Transactions on Computational Biology and Bioinformatics.

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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