Lily H. Peng

1.5k total citations · 1 hit paper
6 papers, 689 citations indexed

About

Lily H. Peng is a scholar working on Radiology, Nuclear Medicine and Imaging, Oncology and Artificial Intelligence. According to data from OpenAlex, Lily H. Peng has authored 6 papers receiving a total of 689 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Radiology, Nuclear Medicine and Imaging, 3 papers in Oncology and 3 papers in Artificial Intelligence. Recurrent topics in Lily H. Peng's work include Radiomics and Machine Learning in Medical Imaging (5 papers), Colorectal Cancer Screening and Detection (3 papers) and AI in cancer detection (3 papers). Lily H. Peng is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (5 papers), Colorectal Cancer Screening and Detection (3 papers) and AI in cancer detection (3 papers). Lily H. Peng collaborates with scholars based in United States, Austria and Thailand. Lily H. Peng's co-authors include Yun Liu, Niels Olson, Jenny L. Smith, Martin C. Stumpe, Arash Mohtashamian, Jason Hipp, George E. Dahl, Timo Kohlberger, Mohammad Norouzi and Greg S. Corrado and has published in prestigious journals such as SHILAP Revista de lepidopterología, JAMA Network Open and Archives of Pathology & Laboratory Medicine.

In The Last Decade

Lily H. Peng

6 papers receiving 670 citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer

2019 302 citations Kunal Nagpal, Davis Foote et al. npj Digital Medicine profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Lily H. Peng United States	6	446	376	134	129	96	6	689
Niels Olson United States	6	467 1.0×	370 1.0×	132 1.0×	137 1.1×	99 1.0×	9	734
Arash Mohtashamian United States	4	386 0.9×	312 0.8×	120 0.9×	118 0.9×	87 0.9×	11	567
Kunal Nagpal United States	5	345 0.8×	258 0.7×	108 0.8×	113 0.9×	109 1.1×	10	592
Chengkuan Chen United States	4	488 1.1×	372 1.0×	65 0.5×	120 0.9×	177 1.8×	5	807
Pooya Mobadersany United States	5	432 1.0×	420 1.1×	84 0.6×	58 0.4×	94 1.0×	9	716
Mane Williams United States	5	431 1.0×	338 0.9×	59 0.4×	87 0.7×	110 1.1×	6	707
Norman Zerbe Germany	12	501 1.1×	314 0.8×	88 0.7×	103 0.8×	194 2.0×	34	780
Thomas de Bel Netherlands	10	641 1.4×	459 1.2×	245 1.8×	103 0.8×	231 2.4×	16	1.0k
Benoît Schmauch France	8	370 0.8×	448 1.2×	76 0.6×	58 0.4×	69 0.7×	14	763
Ole-Johan Skrede United Kingdom	3	319 0.7×	401 1.1×	69 0.5×	78 0.6×	55 0.6×	4	654

Countries citing papers authored by Lily H. Peng

Since Specialization

Citations

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

Fields of papers citing papers by Lily H. Peng

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Lily H. Peng. 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 Lily H. Peng. The network helps show where Lily H. Peng may publish in the future.

Co-authorship network of co-authors of Lily H. Peng

This figure shows the co-authorship network connecting the top 25 collaborators of Lily H. Peng. A scholar is included among the top collaborators of Lily H. Peng 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 Lily H. Peng. Lily H. Peng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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6 of 6 papers shown

1.

Krogue, Justin D., Shekoofeh Azizi, Fraser Elisabeth Tan, et al.. (2023). Predicting lymph node metastasis from primary tumor histology and clinicopathologic factors in colorectal cancer using deep learning. SHILAP Revista de lepidopterología. 3(1). 59–59. 12 indexed citations

2.

L’Imperio, Vincenzo, Ellery Wulczyn, Markus Plass, et al.. (2023). Pathologist Validation of a Machine Learning–Derived Feature for Colon Cancer Risk Stratification. JAMA Network Open. 6(3). e2254891–e2254891. 31 indexed citations

3.

Gamble, Paul, Ronnachai Jaroensri, Hongwu Wang, et al.. (2021). Determining breast cancer biomarker status and associated morphological features using deep learning. Communications Medicine. 1(1). 14–14. 86 indexed citations

4.

Soonthornworasiri, Ngamphol, Chetan Rao, Rajiv Raman, et al.. (2020). Longitudinal Screening for Diabetic Retinopathy in a Nationwide Screening Program: Comparing Deep Learning and Human Graders. Journal of Diabetes Research. 2020. 1–8. 12 indexed citations

5.

Nagpal, Kunal, Davis Foote, Yun Liu, et al.. (2019). Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer. npj Digital Medicine. 2(1). 48–48. 302 indexed citations breakdown →

6.

Liu, Yun, Timo Kohlberger, Mohammad Norouzi, et al.. (2018). Artificial Intelligence–Based Breast Cancer Nodal Metastasis Detection: Insights Into the Black Box for Pathologists. Archives of Pathology & Laboratory Medicine. 143(7). 859–868. 246 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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