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Peter Jack Naylor
Peter Jack Naylor
RIKEN AIP
Verified email at riken.jp - Homepage
Title
Cited by
Cited by
Year
Segmentation of nuclei in histopathology images by deep regression of the distance map
P Naylor, M Laé, F Reyal, T Walter
IEEE transactions on medical imaging 38 (2), 448-459, 2018
5222018
Nuclei segmentation in histopathology images using deep neural networks
P Naylor, M Laé, F Reyal, T Walter
2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017 …, 2017
2292017
Deep learning identifies morphological patterns of homologous recombination deficiency in luminal breast cancers from whole slide images
T Lazard, G Bataillon, P Naylor, T Popova, FC Bidard, D Stoppa-Lyonnet, ...
Cell Reports Medicine 3 (12), 2022
302022
Predicting residual cancer burden in a triple negative breast cancer cohort
P Naylor, J Boyd, M Lae, F Reyal, T Walter
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019 …, 2019
82019
Prediction of Treatment Response in Triple Negative Breast Cancer From Whole Slide Images
P Naylor, T Lazard, G Bataillon, M Laé, A Vincent-Salomon, AS Hamy, ...
Frontiers in Signal Processing 2, 851809, 2022
42022
Optimal Transport for Change Detection on LiDAR Point Clouds
M Fiorucci, P Naylor, M Yamada
arXiv preprint arXiv:2302.07025, 2023
12023
Feature screening with kernel knockoffs
B Poignard, PJ Naylor, H Climente-González, M Yamada
International Conference on Artificial Intelligence and Statistics, 1935-1974, 2022
12022
From cellular phenotypes to the analysis of whole slide images: Application to treatment response in triple-negative breast cancer
P Naylor
Université Paris sciences et lettres, 2019
12019
Deep-Learning-based Change Detection with Spaceborne Hyperspectral PRISMA data
JF Amieva, A Austoni, MA Brovelli, L Ansalone, P Naylor, F Serva, ...
arXiv preprint arXiv:2310.13627, 2023
2023
Implicit neural representation for change detection
P Naylor, D Di Carlo, A Traviglia, M Yamada, M Fiorucci
arXiv preprint arXiv:2307.15428, 2023
2023
Scale dependant layer for self-supervised nuclei encoding
P Naylor, YHH Tsai, M Laé, M Yamada
arXiv preprint arXiv:2207.10950, 2022
2022
Prediction of Homologous Recombination Deficiency of Breast Carcinomas on Digitalized HE Slides Using Machine and Deep Learning Approaches
G Bataillon, A Vincent-Salomon, T Walter, MH Stern, P Naylor, Y Kirova, ...
LABORATORY INVESTIGATION 101 (SUPPL 1), 84-85, 2021
2021
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