Nick Pawlowski
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- Radiomics and Machine Learning in Medical Imaging 1
- Medical Imaging Techniques and Applications 1
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- Medical Image Segmentation Techniques 2
- Advanced Neural Network Applications 2
- Image and Signal Denoising Methods 1
- Artificial Intelligence top 10%
- AI in cancer detection 2
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- Brain Tumor Detection and Classification 2
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- Neural dynamics and brain function 1
- Co-authors
- Ben GlockerNat DilokthanakulKersten PetersenMichiel SchaapMurray ShanahanMartin RajchlDaniel RueckertIoannis Lavdas
- Cited by
- Radiology, Nuclear Medicine and ImagingComputer Vision and Pattern RecognitionArtificial Intelligence
- Partner nations
- United KingdomSwitzerlandUnited States
In The Last Decade
Nick Pawlowski
11 papers receiving 291 citations
Peers
Comparison fields: 5 of 72
- Radiology, Nuclear Medicine and Imaging 127
- Computer Vision and Pattern Recognition 93
- Artificial Intelligence 122
- Health Informatics 4
- Neurology 23
Countries citing papers authored by Nick Pawlowski
This map shows the geographic impact of Nick Pawlowski'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 Nick Pawlowski with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nick Pawlowski more than expected).
Fields of papers citing papers by Nick Pawlowski
This network shows the impact of papers produced by Nick Pawlowski. 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 Nick Pawlowski. The network helps show where Nick Pawlowski may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Nick Pawlowski, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2023 | 6 | |
| 2 | 2021 | 5 | |
| 3 | Deep Structural Causal Models for Tractable Counterfactual Inference | 2020 | 3 |
| 4 | GainForest: Scaling Climate Finance for Forest Conservation using Interpretable Machine Learning on Satellite Imagery | 2019 | 5 |
| 5 | 2019 | 65 | |
| 6 | 2019 | 75 | |
| 7 | Template Transformer Networks for Image Segmentation | 2019 | 3 |
| 8 | Unsupervised Lesion Detection in Brain CT using Bayesian Convolutional Autoencoders | 2018 | 31 |
| 9 | 2018 | 73 | |
| 10 | 2017 | 4 | |
| 11 | 2017 | 29 |
About Nick Pawlowski
Nick Pawlowski is a scholar working on Neurology, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 11 papers that have together received 299 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (2 papers), AI in cancer detection (2 papers), Brain Tumor Detection and Classification (2 papers), Advanced Neural Network Applications (2 papers), Radiomics and Machine Learning in Medical Imaging (1 paper), Image and Signal Denoising Methods (1 paper), Neural dynamics and brain function (1 paper) and Medical Imaging Techniques and Applications (1 paper). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (127 citations), Computer Vision and Pattern Recognition (93 citations) and Artificial Intelligence (122 citations). Nick Pawlowski has collaborated with scholars based in United Kingdom, Switzerland and United States. Frequent co-authors include Ben Glocker, Nat Dilokthanakul, Kersten Petersen, Michiel Schaap, Murray Shanahan, Martin Rajchl, Daniel Rueckert, Ioannis Lavdas, Eric O. Aboagye and Andrea Rockall.
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.