Peter Truszkowski

518 citations
2 papers · 379 · 1 hit paper · h-index 2

Impact in

Papers in

Peter Truszkowski

2 papers receiving 367 citations

Peter Truszkowski's Hit Papers

Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer 2018 · 346 citations
3460+2+5Years since publication100200300

Peers

Peter Truszkowski
Comparison fields: 5 of 61
  • Health Informatics 68
  • Radiology, Nuclear Medicine and Imaging 161
  • Artificial Intelligence 209
  • Biophysics 23
  • Health Information Management 15
Replace Mishka Gidwani with:
Mishka Gidwani United States
Luoting Zhuang United States
Kevin Faust Canada
Ronnachai Jaroensri United States
Marko van Treeck Germany
Pierre Manceron France
Ivy Liang United States
Charles Maussion France
Luca L. Weishaupt United States
Ankush Patel United States
Peter Truszkowski relative to Mishka Gidwani United States Mishka Gidwani's profile →
Citations per field
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Citations per year

Countries citing papers authored by Peter Truszkowski

Since Specialization
Citations

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

Fields of papers citing papers by Peter Truszkowski

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 15 scholars most cited alongside Peter Truszkowski, 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 Peter Truszkowski Line = papers co-authored together Peter Truszkowski links everyone, so they are left out of the graph.

All Works

2 of 2 papers shown
#Work
1
Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer
Hit paper breakdown →
2018346
2 201233

About Peter Truszkowski

Peter Truszkowski is a scholar working on Molecular Biology, Epidemiology, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Immunology and Allergy, having authored 2 papers that have together received 379 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (1 paper), Cell Adhesion Molecules Research (1 paper), AI in cancer detection (1 paper), Cervical Cancer and HPV Research (1 paper), Cancer-related gene regulation (1 paper) and Signaling Pathways in Disease (1 paper). The work is most often cited by research in Health Informatics (68 citations), Radiology, Nuclear Medicine and Imaging (161 citations), Artificial Intelligence (209 citations), Biophysics (23 citations) and Health Information Management (15 citations). Peter Truszkowski has collaborated with scholars based in United States and China. Frequent co-authors include Martin C. Stumpe, Lily Peng, Jason Hipp, Yun Liu, David F. Steiner, Robert MacDonald, Christopher G. Duncan, Ningjing Lin, Patrick Killela and Roger E. McLendon. Their work appears in journals such as Molecular Cancer Research and The American Journal of Surgical Pathology.

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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