Deep learning to analyse microscopy images
| dc.contributor.author | Jacquemet Guillaume | |
| dc.contributor.organization | fi=Turun biotiedekeskus|en=Turku Bioscience Centre| | |
| dc.contributor.organization-code | 1.2.246.10.2458963.20.18586209670 | |
| dc.converis.publication-id | 69310675 | |
| dc.converis.url | https://research.utu.fi/converis/portal/Publication/69310675 | |
| dc.date.accessioned | 2022-10-27T12:24:01Z | |
| dc.date.available | 2022-10-27T12:24:01Z | |
| dc.description.abstract | <p>Artificial intelligence (AI)-powered algorithms are now influencing many aspects of our day-to-day life, from providing movies/music recommendations to controlling self-driving cars. These algorithms are also increasingly used in the lab to aid biomedical research. In particular, the ability to analyse and process images using AI is slowly revolutionizing the quality and quantity of data we collect from microscopy images. In fact, AI-based algorithms can now be applied to perform virtually any high-performance image analysis tasks such as classifying images, detecting and segmenting objects, aligning images or improving image quality by removing noise or increasing image resolution. This short feature article briefly underlies the principles behind using AI algorithms to analyse microscopy images with a specific focus on segmentation and denoising.<br></p> | |
| dc.format.pagerange | 60 | |
| dc.format.pagerange | 64 | |
| dc.identifier.jour-issn | 0954-982X | |
| dc.identifier.olddbid | 175250 | |
| dc.identifier.oldhandle | 10024/158344 | |
| dc.identifier.uri | https://www.utupub.fi/handle/11111/35907 | |
| dc.identifier.urn | URN:NBN:fi-fe2022022420774 | |
| dc.language.iso | en | |
| dc.okm.affiliatedauthor | Jacquemet, Guillaume | |
| dc.okm.discipline | 1182 Biochemistry, cell and molecular biology | en_GB |
| dc.okm.discipline | 1182 Biokemia, solu- ja molekyylibiologia | fi_FI |
| dc.okm.internationalcopublication | not an international co-publication | |
| dc.okm.internationality | International publication | |
| dc.okm.type | B1 Scientific Journal | |
| dc.publisher | Portland Press Ltd | |
| dc.publisher.country | United Kingdom | en_GB |
| dc.publisher.country | Britannia | fi_FI |
| dc.publisher.country-code | GB | |
| dc.relation.doi | 10.1042/bio_2021_167 | |
| dc.relation.ispartofjournal | Biochemist | |
| dc.relation.issue | 5 | |
| dc.relation.volume | 43 | |
| dc.source.identifier | https://www.utupub.fi/handle/10024/158344 | |
| dc.title | Deep learning to analyse microscopy images | |
| dc.year.issued | 2021 |
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