Juhun Lee

Also affiliated with UPMC Health System, University of Pittsburgh Medical Center

Computer ScienceMedicine

Pilot corpus only

This score is computed over theSindex pilot corpus and does not cover the full scientific literature. Scores are relative to papers we have ingested — papers, authors, and institutions outside the pilot are not represented. Methodology.

Top 100%percentile

0Author DataRank

Indexed papers

0in pilot corpus

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Papers

Driven by 6 papers. Top paper: “Cross-Organ, Cross-Modality Transfer Learning: Feasibility Study for Segmentation and Classification”.

Cross-Organ, Cross-Modality Transfer Learning: Feasibility Study for Segmentation and Classification

IEEE Access(2020)10.1109/access.2020.3038909

N/A

0DataRank · unranked

28 citations

Robert M. Nishikawa, Juhun Lee

Identifying Women With Mammographically- Occult Breast Cancer Leveraging GAN-Simulated Mammograms

IEEE Transactions on Medical Imaging(2021)10.1109/tmi.2021.3108949

N/A

0DataRank · unranked

35 citations

Robert M. Nishikawa, Juhun Lee

Developing breast lesion detection algorithms for digital breast tomosynthesis: Leveraging false positive findings

Medical Physics(2022)10.1002/mp.15883

N/A

0DataRank · unranked

16 citations

Robert M. Nishikawa, Juhun Lee, Belayat Hossain

Impact of GAN artifacts for simulating mammograms on identifying mammographically occult cancer

Journal of Medical Imaging(2023)10.1117/1.jmi.10.5.054503

N/A

0DataRank · unranked

9 citations

Tamerlan Mustafaev, Robert M. Nishikawa, Juhun Lee

Improving lesion detection in mammograms by leveraging a Cycle-GAN-based lesion remover

Breast Cancer Research(2024)10.1186/s13058-024-01777-x

N/A

0DataRank · unranked

12 citations

Robert M. Nishikawa, Juhun Lee

Mammo-GAN-Assisted Deep Network Training Scheme for Lesion Detection

Journal of Imaging Informatics in Medicine(2025)10.1007/s10278-025-01771-5

N/A

0DataRank · unranked

0 citations

Robert M. Nishikawa, Juhun Lee