Výsledky vyhledávání - "Äyrämö, Sami"

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Identification of Cognitive Decline from Spoken Language through Feature Selection and the Bag of Acoustic Words Model

Autor: Niemelä, Marko, von Bonsdorff, Mikaela, Äyrämö, Sami, Kärkkäinen, Tommi

Memory disorders are a central factor in the decline of functioning and daily activities in elderly individuals. The confirmation of the illness, initiation of medication to slow its progression, and the commencement of occupational therapy aimed at

Externí odkaz: http://arxiv.org/abs/2402.01824

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Utilizing the International Classification of Functioning, Disability and Health (ICF) in forming a personal health index

Autor: Rautiainen, Ilkka, Parviainen, Lauri, Jakoaho, Veera, Äyrämö, Sami, Kauppi, Jukka-Pekka

We propose a new model for comprehensively monitoring the health status of individuals by calculating a personal health index. The central framework of the model is the International Classification of Functioning, Disability and Health (ICF) develope

Externí odkaz: http://arxiv.org/abs/2304.06143

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Domain-specific transfer learning in the automated scoring of tumor-stroma ratio from histopathological images of colorectal cancer

Autor: Petäinen, Liisa, Väyrynen, Juha P., Ruusuvuori, Pekka, Pölönen, Ilkka, Äyrämö, Sami, Kuopio, Teijo

Tumor-stroma ratio (TSR) is a prognostic factor for many types of solid tumors. In this study, we propose a method for automated estimation of TSR from histopathological images of colorectal cancer. The method is based on convolutional neural network

Externí odkaz: http://arxiv.org/abs/2212.14652

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Predicting overweight and obesity in later life from childhood data: A review of predictive modeling approaches

Autor: Rautiainen, Ilkka, Äyrämö, Sami

Background: Overweight and obesity are an increasing phenomenon worldwide. Predicting future overweight or obesity early in the childhood reliably could enable a successful intervention by experts. While a lot of research has been done using explanat

Externí odkaz: http://arxiv.org/abs/1911.08361

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Akademický článek

Improved accuracy in colorectal cancer tissue decomposition through refinement of established deep learning solutions.

Autor: Prezja, Fabi^1,2 (AUTHOR) faprezja@jyu.fi, Äyrämö, Sami^1,2 (AUTHOR), Pölönen, Ilkka^1,3 (AUTHOR), Ojala, Timo^1,2 (AUTHOR), Lahtinen, Suvi^1,4 (AUTHOR), Ruusuvuori, Pekka^5,6 (AUTHOR), Kuopio, Teijo^7,8,9 (AUTHOR)

Publikováno v: Scientific Reports. 11/20/2023, Vol. 13 Issue 1, p1-14. 14p.