Nuclei cell recognition with deep learning

• Autorzy: Słapek M. , Podpora M.
• Języki publikacji: EN

Abstrakty

EN

The purpose of this work is to create software for recognizing nuclei cells from microscope photos and evaluate quality of it's output using appropri-ate estimation methods. Such software can be used to advance medical discov-ery. The problem can be classified as instance segmentation. The presented solu-tion can be described as Artificial Intelligence software. The software will em-ploy Machine Learning using Convolutional Neural Network.

• Wydawca: Oficyna Wydawnicza Politechniki Opolskiej
• Czasopismo: Zeszyty Naukowe. Elektryka / Politechnika Opolska
• Rocznik: 2018
• Tom: z. 76
• Strony: 89--92
• Opis fizyczny: Bibliogr. 5 poz., rys.

Twórcy

autor

Słapek M.

• Faculty of Electrical Engineering, Automatic Control and Informatics Institute of Computer Science

autor

Podpora M.

• Faculty of Electrical Engineering, Automatic Control and Informatics Institute of Computer Science

Bibliografia

• [1] GOODFELLOW I. et al.: Deep Learning, ISBN 978-0262035613, The MIT Press, 2016
• [2] YU F., KOLTUN V.: Multi-Scale Context Aggregation by Dilated Convolutions, International Conference on Learning Representations, 2016
• [3] LJOSA, V.; SOKOLNICKI K.L., CARPENTER A.E.: Annotated high-throughput microscopy image sets for validation, Nature Methods, Volume 9, Springer Nature, 2012
• [4] LIN T. et al.: Microsoft COCO: Common Objects in Context, Computer Vision – ECCV 2014, Springer International Publishing, 2014
• [5] LORENZO P.R., NALEPA J., RAMOS L.S., PASTOR J.R.: Hyper-parameter Selection in Deep Neural Networks Using Parallel Particle Swarm Optimization, Proceedings of the Genetic and Evolutionary Computation Conference Companion, ACM, 2017

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).