Davis Foote

1.4k citations
2 papers · 463 · 1 hit paper · h-index 2

Impact in

    • Artificial Intelligence in Healthcare and Education
    • AI in cancer detection
    • Reinforcement Learning in Robotics
    • Evolutionary Algorithms and Applications

Papers in

Davis Foote

2 papers receiving 442 citations

Davis Foote's Hit Papers

Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer 2019 · 333 citations
3330+2+4Years since publication100200300

Peers

Davis Foote
Comparison fields: 5 of 65
  • Health Informatics 52
  • Artificial Intelligence 317
  • Radiology, Nuclear Medicine and Imaging 155
  • Biophysics 24
  • Computer Vision and Pattern Recognition 67
Replace Ahmad Naeem with:
Ahmad Naeem Pakistan
Maximilian Alber Germany
Christian Leibig Germany
Michael Gadermayr Austria
Shivam Kalra Canada
Florian Guitton United Kingdom
Syed Jamal Safdar Gardezi Malaysia
Gustav Müller‐Franzes Germany
Mara Graziani Switzerland
Sébastien Jodogne Belgium
Davis Foote relative to Ahmad Naeem Pakistan Ahmad Naeem's profile →
Citations per field
00.5×10×20×30×43.5×
Ahmad Naeem · 1×
Citations per year

Countries citing papers authored by Davis Foote

Since Specialization
Citations

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

Fields of papers citing papers by Davis Foote

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Davis Foote, 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 Davis Foote Line = papers co-authored together Davis Foote links everyone, so they are left out of the graph.

All Works

2 of 2 papers shown

About Davis Foote

Davis Foote is a scholar working on Artificial Intelligence, Automotive Engineering, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging and Infectious Diseases, having authored 2 papers that have together received 463 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (1 paper), Reinforcement Learning in Robotics (1 paper), AI in cancer detection (1 paper), Prostate Cancer Diagnosis and Treatment (1 paper), Adversarial Robustness in Machine Learning (1 paper) and Autonomous Vehicle Technology and Safety (1 paper). The work is most often cited by research in Health Informatics (52 citations), Artificial Intelligence (317 citations), Radiology, Nuclear Medicine and Imaging (155 citations), Biophysics (24 citations) and Computer Vision and Pattern Recognition (67 citations). Davis Foote has collaborated with scholars based in United States, Canada and Belgium. Frequent co-authors include Niels Olson, Mahul B. Amin, Ankur R. Sangoi, Craig H. Mermel, Martin C. Stumpe, Andrew Evans, Kunal Nagpal, Ellery Wulczyn, Jason Hipp and Yun Liu. Their work appears in journals such as npj Digital Medicine and arXiv (Cornell University).

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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