Peter Truszkowski

502 citations
2 papers · 341 indexed · 1 hit paper · h-index 2

Impact in

Papers in

Peter Truszkowski

2 papers receiving 332 citations

Hit Papers

Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer 2018 · 308 citations
3082018202620202023100200300

Peers

Peter Truszkowski
Comparison fields: 5 of 62
  • Health Informatics 66
  • Radiology, Nuclear Medicine and Imaging 187
  • Artificial Intelligence 211
  • Biophysics 24
  • Health Information Management 15
Replace Ivy Liang with:
Ivy Liang United States
Luca L. Weishaupt United States
Luoting Zhuang United States
Charles Maussion France
Manuela Vecsler United States
Kevin Boehm United States
Maha Shady United States
Ankush Patel United States
Amina Bolatkan Japan
Norio Shinkai Japan
Peter Truszkowski relative to Ivy Liang United States Ivy Liang's profile →
Citations per field
00.5×10×20×30×40×49×
Ivy Liang · 1×
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 →
2018308
2 201233

About Peter Truszkowski

Peter Truszkowski is a scholar working on Immunology and Allergy, Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Epidemiology and Molecular Biology, having authored 2 papers that have together received 341 indexed citations. Recurring topics across this work include Cancer-related gene regulation (1 paper), Signaling Pathways in Disease (1 paper), Cervical Cancer and HPV Research (1 paper), Cell Adhesion Molecules Research (1 paper), Radiomics and Machine Learning in Medical Imaging (1 paper) and AI in cancer detection (1 paper). The work is most often cited by research in Health Informatics (66 citations), Radiology, Nuclear Medicine and Imaging (187 citations), Artificial Intelligence (211 citations), Biophysics (24 citations) and Health Information Management (15 citations). Peter Truszkowski has collaborated with scholars based in United States and China. Frequent co-authors include Lily Peng, Robert MacDonald, Jason Hipp, Martin C. Stumpe, David F. Steiner, Yun Liu, Chunhui Di, Darell D. Bigner, David Cory Adamson and Patrick Killela. 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.

Explore authors with similar magnitude of impact

Rankless by CCL
2026