Semi-supervised training of cell-classifier neural networks
Nowadays, microscopes used in biological research produce a huge amount of image data. Manually processing the images is a very time-consuming and resource-heavy task, so the development and implementation of new automatic systems is required. Moreover, as we have access to a large amount of unlabel...
Elmentve itt :
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| Testületi szerző: | |
| Dokumentumtípus: | Könyv része |
| Megjelent: |
2018
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| Sorozat: | Conference of PhD Students in Computer Science
11 |
| Kulcsszavak: | Számítástechnika, Biológiai kutatás |
| Online Access: | http://acta.bibl.u-szeged.hu/61772 |
| LEADER | 01436naa a2200217 i 4500 | ||
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| 040 | |a SZTE Egyetemi Kiadványok Repozitórium |b hun | ||
| 041 | |a zxx | ||
| 100 | 1 | |a Pap Gergely | |
| 245 | 1 | 0 | |a Semi-supervised training of cell-classifier neural networks |h [elektronikus dokumentum] / |c Pap Gergely |
| 260 | |c 2018 | ||
| 300 | |a 84-87 | ||
| 490 | 0 | |a Conference of PhD Students in Computer Science |v 11 | |
| 520 | 3 | |a Nowadays, microscopes used in biological research produce a huge amount of image data. Manually processing the images is a very time-consuming and resource-heavy task, so the development and implementation of new automatic systems is required. Moreover, as we have access to a large amount of unlabeled data, while labels are only available for a small subset, these novel methods should be able to process large amounts of unlabeled data with minimal manual supervision. Here, we apply neural networks to classify cells present in biological images, and show that their accuracy can be improved by using semi-supervised training algorithms. | |
| 695 | |a Számítástechnika, Biológiai kutatás | ||
| 700 | 0 | 1 | |a Grósz Tamás |e aut |
| 700 | 0 | 1 | |a Tóth László |e aut |
| 710 | |a Conference of PhD students in computer science (11.) (2018) (Szeged) | ||
| 856 | 4 | 0 | |u http://acta.bibl.u-szeged.hu/61772/1/cscs_2018_097-100.pdf |z Dokumentum-elérés |