Deep learning to analyse microscopy images

dc.contributor.authorJacquemet Guillaume
dc.contributor.organizationfi=Turun biotiedekeskus|en=Turku Bioscience Centre|
dc.contributor.organization-code1.2.246.10.2458963.20.18586209670
dc.converis.publication-id69310675
dc.converis.urlhttps://research.utu.fi/converis/portal/Publication/69310675
dc.date.accessioned2022-10-27T12:24:01Z
dc.date.available2022-10-27T12:24:01Z
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.pagerange60
dc.format.pagerange64
dc.identifier.jour-issn0954-982X
dc.identifier.olddbid175250
dc.identifier.oldhandle10024/158344
dc.identifier.urihttps://www.utupub.fi/handle/11111/35907
dc.identifier.urnURN:NBN:fi-fe2022022420774
dc.language.isoen
dc.okm.affiliatedauthorJacquemet, Guillaume
dc.okm.discipline1182 Biochemistry, cell and molecular biologyen_GB
dc.okm.discipline1182 Biokemia, solu- ja molekyylibiologiafi_FI
dc.okm.internationalcopublicationnot an international co-publication
dc.okm.internationalityInternational publication
dc.okm.typeB1 Scientific Journal
dc.publisherPortland Press Ltd
dc.publisher.countryUnited Kingdomen_GB
dc.publisher.countryBritanniafi_FI
dc.publisher.country-codeGB
dc.relation.doi10.1042/bio_2021_167
dc.relation.ispartofjournalBiochemist
dc.relation.issue5
dc.relation.volume43
dc.source.identifierhttps://www.utupub.fi/handle/10024/158344
dc.titleDeep learning to analyse microscopy images
dc.year.issued2021

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