Nick Pawlowski

1.9k total citations
11 papers, 299 citations indexed

About

Nick Pawlowski is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Nick Pawlowski has authored 11 papers receiving a total of 299 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 4 papers in Computer Vision and Pattern Recognition and 4 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Nick Pawlowski's work include Medical Image Segmentation Techniques (2 papers), AI in cancer detection (2 papers) and Brain Tumor Detection and Classification (2 papers). Nick Pawlowski is often cited by papers focused on Medical Image Segmentation Techniques (2 papers), AI in cancer detection (2 papers) and Brain Tumor Detection and Classification (2 papers). Nick Pawlowski collaborates with scholars based in United Kingdom, Switzerland and United States. Nick Pawlowski's 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 and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Medical Imaging and IEEE Transactions on Neural Networks and Learning Systems.

In The Last Decade

Nick Pawlowski

11 papers receiving 291 citations

Peers

Nick Pawlowski

RNMI

AI

CVPR

BE

EEE

Tarun Agrawal India

Junwei Yang China

Guangyuan Li China

Rayan Krishnan United States

M.W. Roth China

Qing Lyu China

Lingzhi Hu China

Wenqing Sun China

Siddhartha Nath India

Tarun Agrawal India

Nick Pawlowski

119 ×0.9

RNMI

80 ×0.7

AI

67 ×0.7

CVPR

27 ×0.6

BE

42 ×1.3

EEE

Citations per year, relative to Nick Pawlowski Nick Pawlowski (= 1×) peers Tarun Agrawal

Countries citing papers authored by Nick Pawlowski

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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 of co-authors of Nick Pawlowski

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

All Works

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11 of 11 papers shown

1.

Menten, Martin J., Robbie Holland, Hrvoje Bogunović, et al.. (2023). Exploring Healthy Retinal Aging with Deep Learning. SHILAP Revista de lepidopterología. 3(3). 100294–100294. 6 indexed citations

2.

Chen, Xiaoran, Nick Pawlowski, Ben Glocker, & Ender Konukoglu. (2021). Normative ascent with local gaussians for unsupervised lesion detection. Medical Image Analysis. 74. 102208–102208. 5 indexed citations

3.

Pawlowski, Nick, Daniel C. Castro, & Ben Glocker. (2020). Deep Structural Causal Models for Tractable Counterfactual Inference. Spiral (Imperial College London). 33. 857–869. 3 indexed citations

4.

Fung, Clement, et al.. (2019). GainForest: Scaling Climate Finance for Forest Conservation using Interpretable Machine Learning on Satellite Imagery. International Conference on Machine Learning. 5 indexed citations

5.

Dilokthanakul, Nat, et al.. (2019). Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning. IEEE Transactions on Neural Networks and Learning Systems. 30(11). 3409–3418. 65 indexed citations

6.

Petersen, Kersten, et al.. (2019). TeTrIS: Template Transformer Networks for Image Segmentation With Shape Priors. IEEE Transactions on Medical Imaging. 38(11). 2596–2606. 75 indexed citations

7.

Petersen, Kersten, et al.. (2019). Template Transformer Networks for Image Segmentation. 3 indexed citations

8.

Pawlowski, Nick, Matthew C. H. Lee, Martin Rajchl, et al.. (2018). Unsupervised Lesion Detection in Brain CT using Bayesian Convolutional Autoencoders. 31 indexed citations

9.

Valindria, Vanya V., Nick Pawlowski, Martin Rajchl, et al.. (2018). Multi-modal Learning from Unpaired Images: Application to Multi-organ Segmentation in CT and MRI. 547–556. 73 indexed citations

10.

Pawlowski, Nick, et al.. (2017). Efficient variational Bayesian neural network ensembles for outlier detection. arXiv (Cornell University). 4 indexed citations

11.

Pawlowski, Nick, et al.. (2017). A portable diagnostic device for cardiac magnetic field mapping. Biomedical Physics & Engineering Express. 3(1). 15008–15008. 29 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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