Joohyung Lee

1.3k citations
38 papers · 519 indexed · h-index 12

Joohyung Lee

34 papers receiving 491 citations

Peers

Joohyung Lee
Comparison fields: 5 of 40
  • Artificial Intelligence 434
  • Radiology, Nuclear Medicine and Imaging 122
  • Computational Theory and Mathematics 52
  • Health Informatics 4
  • Oncology 75
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Nitish Shirish Keskar United States
María Rosa Casals Spain
Mustain Billah Bangladesh
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R Schulz-Wendtland Germany
Satyajit Mahapatra India
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Citations per year

Countries citing papers authored by Joohyung Lee

Since Specialization
Citations

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

Fields of papers citing papers by Joohyung Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Joohyung Lee, 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 Joohyung Lee Line = papers co-authored together Joohyung Lee links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20210
2
Multi-Task Learning with a Fully Convolutional Network for Rectum and Rectal Cancer Segmentation.
20191
3 20194
4 201957
5
Weight learning in a probabilistic extension of answer set programs
20183
6 201846
7
A Probabilistic Extension of the Stable Model Semantics
201516
8
Markov Logic Style Weighted Rules under the Stable Model Semantics.
20156
9
Stable models of formulas with intensional functions
201211
10 201219
11 201211
12 201112
13
First-Order Semantics of Aggregates in Answer Set Programming Via Modified Circumscription
20114
14
Integrating rules and ontologies in the first-order stable model semantics
20113
15 201097
16
A reductive semantics for counting and choice in answer set programming
200822
17
A new perspective on stable models
200756
18
A Knowledge Module: Buying and Selling.
20062
19 20051
20
A model-theoretic counterpart of loop formulas
200539

About Joohyung Lee

Joohyung Lee is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Radiology, Nuclear Medicine and Imaging, having authored 38 papers that have together received 519 indexed citations. Recurring topics across this work include Logic, Reasoning, and Knowledge (30 papers), Multi-Agent Systems and Negotiation (22 papers), Logic, programming, and type systems (18 papers), Semantic Web and Ontologies (9 papers), Radiomics and Machine Learning in Medical Imaging (6 papers), Formal Methods in Verification (4 papers), Bayesian Modeling and Causal Inference (3 papers) and Advanced Algebra and Logic (2 papers). The work is most often cited by research in Artificial Intelligence (434 citations), Radiology, Nuclear Medicine and Imaging (122 citations) and Computational Theory and Mathematics (52 citations). Joohyung Lee has collaborated with scholars based in United States, South Korea and Germany. Frequent co-authors include Vladimir Lifschitz, Paolo Ferraris, Dae Kyung Sohn, Ji Eun Oh, Bo Yun Hur, Hee Jin Chang, Roberta Palla, Min Ju Kim, Hong‐Jun Yoon and Seonhye Lee. Their work appears in journals such as IEEE Access, Artificial Intelligence and Journal of Artificial Intelligence Research.

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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