prikaz prve stranice dokumenta Interpretable machine learning approach for neuron-centric analysis of human cortical cytoarchitecture
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Interpretable machine learning approach for neuron-centric analysis of human cortical cytoarchitecture
Scientific Reports, 13 (2023), 1; 5567. https://doi.org/10.1038/s41598-023-32154-x
Štajduhar, Andrija; Lipić, Tomislav; Lončarić, Sven; Judaš, Miloš; Sedmak, Goran

Institucijski repozitorij: Dr Med - digitalni repozitorij Medicinskog fakulteta Sveučilišta u Zagrebu

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Štajduhar, A., Lipić, T., Lončarić, S., Judaš, M. i Sedmak, G. (2023). Interpretable machine learning approach for neuron-centric analysis of human cortical cytoarchitecture. Scientific Reports, 13. (1). doi: 10.1038/s41598-023-32154-x

Štajduhar, Andrija, et al. "Interpretable machine learning approach for neuron-centric analysis of human cortical cytoarchitecture." Scientific Reports, vol. 13, br. 1, 2023. https://doi.org/10.1038/s41598-023-32154-x

Štajduhar, Andrija, Tomislav Lipić, Sven Lončarić, Miloš Judaš i Goran Sedmak. "Interpretable machine learning approach for neuron-centric analysis of human cortical cytoarchitecture." Scientific Reports 13, br. 1 (2023). https://doi.org/10.1038/s41598-023-32154-x

Štajduhar, A., et al. (2023) 'Interpretable machine learning approach for neuron-centric analysis of human cortical cytoarchitecture', Scientific Reports, 13(1). doi: 10.1038/s41598-023-32154-x

Štajduhar A, Lipić T, Lončarić S, Judaš M, Sedmak G. Interpretable machine learning approach for neuron-centric analysis of human cortical cytoarchitecture. Scientific Reports [Internet]. 05.04.2023. [pristupljeno 29.12.2024.];13(1). doi: 10.1038/s41598-023-32154-x

A. Štajduhar, T. Lipić, S. Lončarić, M. Judaš i G. Sedmak, "Interpretable machine learning approach for neuron-centric analysis of human cortical cytoarchitecture", Scientific Reports, vol. 13, br. 1, Travanj 2023. [Online]. Dostupno na: https://urn.nsk.hr/urn:nbn:hr:105:004091. [Citirano: 29.12.2024.]

Za citiranje koristite ovu mrežnu adresu: https://urn.nsk.hr/urn:nbn:hr:105:004091
Podaci o radu
Naslov (engleski)Interpretable machine learning approach for neuron-centric analysis of human cortical cytoarchitecture
AutorAndrija Štajduhar
AutorTomislav Lipić
AutorSven Lončarić
AutorMiloš Judaš
AutorGoran Sedmak
Autorova ustanovaSveučilište u Zagrebu
Medicinski fakultet
(HRVATSKI INSTITUT ZA ISTRAŽIVANJE MOZGA)
Autorova ustanovaSveučilište u Zagrebu
Medicinski fakultet
(KATEDRA ZA ANATOMIJU)
Autorova ustanovaSveučilište u Zagrebu
Medicinski fakultet
(KATEDRA ZA MEDICINSKU STATISTIKU, EPIDEMIOLOGIJU I MEDICINSKU INFORMATIKU)
Znanstveno / umjetničko
područje, polje i grana		BIOMEDICINA I ZDRAVSTVO
Javno zdravstvo i zdravstvena zaštita
Epidemiologija
Znanstveno / umjetničko
područje, polje i grana		BIOMEDICINA I ZDRAVSTVO
Temeljne medicinske znanosti
Anatomija
Znanstveno / umjetničko
područje, polje i grana		BIOMEDICINA I ZDRAVSTVO
Temeljne medicinske znanosti
Neuroznanost
Sažetak (engleski)
The complexity of the cerebral cortex underlies its function and distinguishes us as humans. Here, we present a principled veridical data science methodology for quantitative histology that shifts focus from image-level investigations towards neuron-level representations of cortical regions, with the neurons in the image as a subject of study, rather than pixel-wise image content. Our methodology relies on the automatic segmentation of neurons across whole histological sections and an extensive set of engineered features, which reflect the neuronal phenotype of individual neurons and the properties of neurons’ neighborhoods. The neuron-level representations are used in an interpretable machine learning pipeline for mapping the phenotype to cortical layers. To validate our approach, we created a unique dataset of cortical layers manually annotated by three experts in neuroanatomy and histology. The presented methodology offers high interpretability of the results, providing a deeper understanding of human cortex organization, which may help formulate new scientific hypotheses, as well as to cope with systematic uncertainty in data and model predictions.
Ključne riječi (engleski)
Jezikengleski
Vrsta publikacijeZnanstveni rad - Izvorni znanstveni rad
Status objaveObjavljen
Vrsta recenzijeRecenziran
Verzija publikacijeObjavljena verzija rada (izdavačev PDF)
Naslov časopisaScientific Reports
Brojčani podacivol. 13, br. 1, 5567
e-ISSN2045-2322
DOIhttps://doi.org/10.1038/s41598-023-32154-x
URN:NBNurn:nbn:hr:105:004091
Datum objave publikacije2023-04-05
URL dokumentahttps://www.nature.com/articles/s41598-023-32154-x
Vrsta resursaTekst
Prava pristupaOtvoreni pristup
Uvjeti korištenjaIkona licence Imenovanje 4.0 međunarodna (CC BY 4.0)
Datum i vrijeme pohrane2024-01-08 08:33:40