12 citations as recorded by crossref.

1. Automatic taxonomic identification based on the Fossil Image Dataset (>415,000 images) and deep convolutional neural networks X. Liu et al. 10.1017/pab.2022.14
2. Semantic segmentation of vertebrate microfossils from computed tomography data using a deep learning approach Y. Hou et al. 10.5194/jm-40-163-2021
3. Towards a Fleet of Robots for Orientation, Imaging, and Morphometric Analyses of Planktonic Foraminifera M. Knappertsbusch & J. Eisenecker 10.3389/fmars.2022.798002
4. Diversity of polycystine radiolarians in sediment traps from the Ionian, North Aegean and Cretan Seas: A preliminary account T. Danelian et al. 10.1016/j.revmic.2022.100606
5. No dramatic changes observed in subtropical radiolarian plankton assemblages during the Middle Eocene Climatic Optimum (MECO); evidence from the North Atlantic ODP Site 1051 M. Meunier & T. Danelian 10.1016/j.marmicro.2023.102272
6. Convolutional neural network application on a new middle Eocene radiolarian dataset V. Carlsson et al. 10.1016/j.marmicro.2023.102268
7. Merging databases for CNN image recognition, increasing bias or improving results? M. Tetard et al. 10.1016/j.marmicro.2023.102296
8. A user‐friendly method to get automated pollen analysis from environmental samples B. Gimenez et al. 10.1111/nph.19857
9. Automated detection of microfossil fish teeth from slide images using combined deep learning models K. Mimura et al. 10.1016/j.acags.2022.100092
10. Morphometrics and machine learning discrimination of the middle Eocene radiolarian species Podocyrtis chalara, Podocyrtis goetheana and their morphological intermediates F. Pinto et al. 10.1016/j.marmicro.2023.102293
11. Artificial intelligence applied to the classification of eight middle Eocene species of the genus Podocyrtis (polycystine radiolaria) V. Carlsson et al. 10.5194/jm-41-165-2022
12. Diatom and radiolarian biostratigraphy in the Pliocene sequence of ODP Site 697 (Jane Basin, Atlantic sector of the Southern Ocean) Y. Kato et al. 10.5194/jm-43-93-2024