Sangheum Hwang
- Radiology, Nuclear Medicine and Imaging top 5%
- Artificial Intelligence top 5%
- Pulmonary and Respiratory Medicine
- Computer Vision and Pattern Recognition top 5%
- Health Informatics top 1%
- Co-authors
- Hyoeun KimJi-Hoon JeongHee Jin KimJae Ho SohnJu Gang NamSunggyun ParkJong Hyuk LeeChang Min Park
- Topics
- Advanced Neural Network Applications (4 papers)Industrial Vision Systems and Defect Detection (4 papers)COVID-19 diagnosis using AI (4 papers)
- Partner nations
- South KoreaUnited States
In The Last Decade
Sangheum Hwang
27 papers receiving 768 citations
Hit Papers
Peers
Comparison fields: 5 of 114
- Radiology, Nuclear Medicine and Imaging 459
- Artificial Intelligence 266
- Pulmonary and Respiratory Medicine 195
- Computer Vision and Pattern Recognition 150
- Health Informatics 99
Countries citing papers authored by Sangheum Hwang
This map shows the geographic impact of Sangheum Hwang'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 Sangheum Hwang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sangheum Hwang more than expected).
Fields of papers citing papers by Sangheum Hwang
This network shows the impact of papers produced by Sangheum Hwang. 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 Sangheum Hwang. The network helps show where Sangheum Hwang may publish in the future.
Co-authorship network of co-authors of Sangheum Hwang
This figure shows the co-authorship network connecting the top 25 collaborators of Sangheum Hwang. A scholar is included among the top collaborators of Sangheum Hwang 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 Sangheum Hwang. Sangheum Hwang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 4 | |
| 4 | 0 | |
| 5 | 2 | |
| 6 | 6 | |
| 7 | 3 | |
| 8 | 6 | |
| 9 | 4 | |
| 10 | 4 | |
| 11 | 134 | |
| 12 | 4 | |
| 13 | 14 | |
| 14 | BERT-based Classification Model for Korean Documents | 3 |
| 15 | Development and Validation of Deep Learning–based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographsbreakdown → | 382 |
| 16 | Scale-Invariant Feature Learning using Deconvolutional Neural Networks for Weakly-Supervised Semantic Segmentation. | 11 |
| 17 | 146 | |
| 18 | 8 | |
| 19 | 7 | |
| 20 | 6 |
About Sangheum Hwang
Sangheum Hwang is a scholar working on Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering and Artificial Intelligence, having authored 28 papers that have together received 800 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (4 papers), Industrial Vision Systems and Defect Detection (4 papers) and COVID-19 diagnosis using AI (4 papers). The work is most often cited by research in Health Informatics (99 citations), Radiology, Nuclear Medicine and Imaging (459 citations) and Artificial Intelligence (266 citations). Sangheum Hwang has collaborated with scholars based in South Korea and United States. Frequent co-authors include Hyoeun Kim, Ji-Hoon Jeong, Hee Jin Kim, Jae Ho Sohn, Ju Gang Nam, Sunggyun Park, Jong Hyuk Lee, Chang Min Park, Eui Jin Hwang and Kwang-Nam Jin. Their work appears in journals such as Applied Physics Letters, Cancer Research and Radiology.
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.