Towards detection and classification of microscopic foraminifera using transfer learning
DOI:
https://doi.org/10.7557/18.5144Keywords:
machine learning, deep learning, transfer learning, feature extraction, foraminiferaAbstract
Foraminifera are single-celled marine organisms, which may have a planktic or benthic lifestyle. During their life cycle they construct shells consisting of one or more chambers, and these shells remain as fossils in marine sediments. Classifying and counting these fossils have become an important tool in e.g. oceanography and climatology.
Currently the process of identifying and counting microfossils is performed manually using a microscope and is very time consuming. Developing methods to automate this process is therefore considered important across a range of research fields.
The first steps towards developing a deep learning model that can detect and classify microscopic foraminifera are proposed. The proposed model is based on a VGG16 model that has been pretrained on the ImageNet dataset, and adapted to the foraminifera task using transfer learning. Additionally, a novel image dataset consisting of microscopic foraminifera and sediments from the Barents Sea region is introduced.
References
S. Aagaard-Sørensen, K. Husum, K. Werner, R. F. Spielhagen, M. Hald, and T. M. Marchitto. A late glacial–early holocene multiproxy record from the eastern fram strait, polar north atlantic. Marine Geology, 355:15–26, 2014.
Y. Bengio, A. Courville, and P. Vincent. Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence, 35(8):1798–1828, 2013.
R. S. Boardman, A. H. Cheetham, and A. J. Rowell. Fossil invertebrates. Blackwell Scien- tific Publications, 1987.
T. de Garidel-Thoron, R. Marchant, E. Soto, Y. Gally, L. Beaufort, C. T. Bolton, M. Bouslama, L. Licari, J.-C. Mazur, J.-M. Brutti, et al. Automatic picking of foraminifera: Design of the foraminifera image recognition and sorting tool (first) prototype and results of the image classification scheme. In AGU Fall Meeting Abstracts, 2017.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, pages 248–255. IEEE, 2009.
F. Frontalini and R. Coccioni. Benthic foraminifera as bioindicators of pollution: a review of italian research over the last three decades. Revue de micropaléontologie, 54(2):115–127, 2011.
Y. Gal and Z. Ghahramani. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In ICML, pages 1050–1059, 2016.
Q. Ge, B. Zhong, B. Kanakiya, R. Mitra, T. Marchitto, and E. Lobaton. Coarse-to-fine foraminifera image segmentation through 3d and deep features. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pages 1–8. IEEE, 2017.
M. Hald, C. Andersson, H. Ebbesen, E. Jansen, D. Klitgaard-Kristensen, B. Risebrobakken, G. R. Salomonsen, M. Sarnthein, H. P. Sejrup, and R. J. Telford. Variations in temperature and extent of atlantic water in the northern north atlantic during the holocene. Quaternary Science Reviews, 26(25-28):3423–3440, 2007.
D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. arXiv:1412.6980, 2014.
R. Mitra, T. Marchitto, Q. Ge, B. Zhong, B. Kanakiya, M. Cook, J. Fehrenbacher, J. Ortiz, A. Tripati, and E. Lobaton. Automated species-level identification of planktic foraminifera using convolutional neural networks, with comparison to human performance. Marine Micropaleontology, 147:16–24, 2019.
K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556, 2014.
A. Singh. Micropaleontology in petroleum exploration. In 7th International Conference and Exposition of Petroleum Geophysics, pages 14– 16, 2008.
R. F. Spielhagen, K. Werner, S. A. Sørensen, K. Zamelczyk, E. Kandiano, G. Budeus, K. Husum, T. M. Marchitto, and M. Hald. Enhanced modern heat transfer to the arctic by warm atlantic water. Science, 331(6016):450– 453, 2011.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov. Dropout: a simple way to prevent neural networks from overfitting. JMLR, 15(1):1929–1958, 2014.
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson. How transferable are features in deep neural networks? In NeurIPS, pages 3320–3328, 2014.
B. Zhong, Q. Ge, B. Kanakiya, R. M. T. Marchitto, and E. Lobaton. A comparative study of image classification algorithms for foraminifera identification. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pages 1– 8. IEEE, 2017.
