Nuclei segmentation in histopathology images using deep neural networks. - Peter Naylor; Marick Lae; Fabien Reyal; Thomas Walter 003 (ISBI, 2017)

Predicting Residual Cancer Burden In A Triple Negative Breast Cancer Cohort. - Peter Naylor; Joseph Boyd; Marick Laé; Fabien Reyal; Thomas Walter 003 (ISBI, 2019)

Segmentation of Nuclei in Histopathology Images by Deep Regression of the Distance Map. - Peter Naylor; Marick Lae; Fabien Reyal; Thomas Walter 003 (IEEE Trans. Medical Imaging, 2019)

From cellular phenotypes to the analysis of whole slide images: Application to treatment response in triple-negative breast cancer. (Du phénotypage cellulaire à la classification de lames digitales: Une application au traitement du cancer du sein triple-négatif). - Peter Naylor (2019)

Feature screening with kernel knockoffs. - Benjamin Poignard; Peter J. Naylor; Héctor Climente-González; Makoto Yamada (AISTATS, 2022)

Scale dependant layer for self-supervised nuclei encoding. - Peter Naylor; Yao-Hung Hubert Tsai; Marick Laé; Makoto Yamada (CoRR, 2022)

Segmentation of Nuclei in Histopathology Images by deep regression of the distance map. - Peter Naylor; Marick Laé; Fabien Reyal; Thomas Walter 003 (IEEE DataPort, 2022)

Optimal Transport for Change Detection on Lidar Point Clouds. - Marco Fiorucci; Peter Naylor; Makoto Yamada (IGARSS, 2023)

Implicit Assimilation of Sparse In Situ Data for Dense & Global Storm Surge Forecasting. - Patrick Ebel 002; Brandon Victor; Peter Naylor; Gabriele Meoni; Federico Serva; Rochelle Schneider (CVPR Workshops, 2024)

Off to new Shores: A Dataset & Benchmark for (near-)coastal Flood Inundation Forecasting. - Brandon Victor; Mathilde Letard; Peter Naylor; Karim Douch; Nicolas Longépé; Zhen He 002; Patrick Ebel 002 (NeurIPS, 2024)

Implicit neural representation for change detection. - Peter Naylor; Diego Di Carlo; Arianna Traviglia; Makoto Yamada; Marco Fiorucci (WACV, 2024)