Paolo Ocampo

4.1k total citations · 1 hit paper
9 papers, 2.2k citations indexed

About

Paolo Ocampo is a scholar working on Artificial Intelligence, Physiology and Genetics. According to data from OpenAlex, Paolo Ocampo has authored 9 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Artificial Intelligence, 3 papers in Physiology and 3 papers in Genetics. Recurrent topics in Paolo Ocampo's work include AI in cancer detection (3 papers), Syphilis Diagnosis and Treatment (3 papers) and Radiomics and Machine Learning in Medical Imaging (1 paper). Paolo Ocampo is often cited by papers focused on AI in cancer detection (3 papers), Syphilis Diagnosis and Treatment (3 papers) and Radiomics and Machine Learning in Medical Imaging (1 paper). Paolo Ocampo collaborates with scholars based in United States, Greece and United Kingdom. Paolo Ocampo's co-authors include David Fenyö, André L. Moreira, Nicolas Coudray, Aristotelis Tsirigos, Theodore Sakellaropoulos, Navneet Narula, Matija Snuderl, Narges Razavian, Pia Abel zur Wiesch and Csaba Pál and has published in prestigious journals such as Nature Medicine, Proceedings of the Royal Society B Biological Sciences and Antimicrobial Agents and Chemotherapy.

In The Last Decade

Paolo Ocampo

9 papers receiving 2.1k citations

Hit Papers

Classification and mutation prediction from non–small cel... 2018 2026 2020 2023 2018 500 1000 1.5k
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.

  1. Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning
    2018 1.7k citations Nicolas Coudray, Paolo Ocampo et al. Nature Medicine profile →

Peers

Paolo Ocampo
Nina Linder Finland
Famke Aeffner United States
Natalie Shih United States
Peter Hufnagl Germany
Jia Wu United States
A. K. Bhattacharyya United States
Xiaowei Zhan United States
Petros Karakitsos Greece
Carlo Russo United States
Miao Cui China
Nina Linder Finland
Paolo Ocampo
Citations per year, relative to Paolo Ocampo Paolo Ocampo (= 1×) peers Nina Linder

Countries citing papers authored by Paolo Ocampo

Since Specialization
Citations

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

Fields of papers citing papers by Paolo Ocampo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Paolo Ocampo

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

All Works

9 of 9 papers shown
1.
Kiên, Nguyễn Trung, et al.. (2022). Attention-Based Deep Multiple Instance Learning with Adaptive Instance Sampling. 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). 1–5. 2 indexed citations
2.
Ocampo, Paolo, André L. Moreira, Nicolas Coudray, et al.. (2018). P1.09-32 Classification and Mutation Prediction from Non-Small Cell Lung Cancer Histopathology Images Using Deep Learning. Journal of Thoracic Oncology. 13(10). S562–S562. 13 indexed citations
3.
Coudray, Nicolas, Paolo Ocampo, Theodore Sakellaropoulos, et al.. (2018). Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning. Nature Medicine. 24(10). 1559–1567. 1738 indexed citations breakdown →
4.
Wiesch, Pia Abel zur, Sören Abel, Spyridon Gkotzis, et al.. (2015). Classic reaction kinetics can explain complex patterns of antibiotic action. Science Translational Medicine. 7(287). 287ra73–287ra73. 56 indexed citations
5.
Ocampo, Paolo, Viktória Lázár, Balázs Papp, et al.. (2014). Antagonism between Bacteriostatic and Bactericidal Antibiotics Is Prevalent. Antimicrobial Agents and Chemotherapy. 58(8). 4573–4582. 203 indexed citations
6.
Ocampo, Paolo, et al.. (2010). An experimental study of the population and evolutionary dynamics ofVibrio choleraeO1 and the bacteriophage JSF4. Proceedings of the Royal Society B Biological Sciences. 277(1698). 3247–3254. 22 indexed citations
7.
8.
Harper, Kristin N., Paolo Ocampo, B M Steiner, et al.. (2008). On the Origin of the Treponematoses: A Phylogenetic Approach. PLoS neglected tropical diseases. 2(1). e148–e148. 107 indexed citations
9.
Armelagos, George J., Kristin N. Harper, & Paolo Ocampo. (2005). On the Trail of the Twisted Treponeme: Searching for the Origins of Syphilis. Evolutionary Anthropology Issues News and Reviews. 14(6). 240–242. 5 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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