| Federated learning enables big data for rare cancer boundary detection S Pati, U Baid, B Edwards, M Sheller, SH Wang, GA Reina, P Foley, ... Nature communications 13 (1), 7346, 2022 | 141 | 2022 |
| The Cell Tracking Challenge: 10 years of objective benchmarking M Maška, V Ulman, P Delgado-Rodriguez, E Gómez-de-Mariscal, ... Nature Methods 20 (7), 1010-1020, 2023 | 50 | 2023 |
| DIC image segmentation of dense cell populations by combining deep learning and watershed F Lux, P Matula 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019 …, 2019 | 41 | 2019 |
| Cell segmentation by combining marker-controlled watershed and deep learning F Lux, P Matula arXiv preprint arXiv:2004.01607, 2020 | 27 | 2020 |
| Biomedical image analysis competitions: The state of current participation practice M Eisenmann, A Reinke, V Weru, MD Tizabi, F Isensee, TJ Adler, ... arXiv preprint arXiv:2212.08568, 2022 | 23 | 2022 |
| Author Correction: Federated learning enables big data for rare cancer boundary detection S Pati, U Baid, B Edwards, M Sheller, SH Wang, GA Reina, P Foley, ... nature communications 14 (1), 436, 2023 | 3 | 2023 |
| Mu-lux-cz F Lux, P Matula Cell Tracking Challenge, 2020 | 3 | 2020 |
| Segmentation of Dense Cell Populations Using Convolutional Neural Networks F Lux | 2 | 2018 |
| Federated learning enables big data for rare cancer boundary detection (vol 13, 7346, 2022) S Pati, U Baid, B Edwards, M Sheller, SH Wang, GA Reina, P Foley, ... NATURE PORTFOLIO, 2023 | | 2023 |
| Shape-aware Segmentation of Biomedical Images using Deep Learning F Lux | | |
