Now showing items 1-4 of 4

    • Automatic MRI Quantifying Methods in Behavioral-Variant Frontotemporal Dementia Diagnosis 

      Cajanus, A; Hall, A; Koikkalainen, J; Solje, E; Tolonen, A; Urhemaa, T; Liu, Y; Haanpää, RM; Hartikainen, P; Helisalmi, S; Korhonen, V; Rueckert, D; Hasselbalch, S; Waldemar, G; Mecocci, P; Vanninen, R; van Gils, M; Soininen, H; Lötjönen, J; Remes, AM (S. Karger AG, 2018)
      Aims: We assessed the value of automated MRI quantification methods in the differential diagnosis of behavioral-variant frontotemporal dementia (bvFTD) from Alzheimer disease (AD), Lewy body dementia (LBD), and subjective ...
      Tieteelliset aikakauslehtiartikkelit

    • Comparison of feature representations in MRI-based MCI-to-AD conversion prediction 

      Gómez-Sancho, Marta; Tohka, Jussi; Gómez-Verdejo Vanessa (Elsevier BV, 2018)
      Alzheimer's disease (AD) is a progressive neurological disorder in which the death of brain cells causes memory loss and cognitive decline. The identification of at-risk subjects yet showing no dementia symptoms but who ...
      Tieteelliset aikakauslehtiartikkelit

    • Machine learning augmented near-infrared spectroscopy: In vivo follow-up of cartilage defects 

      Sarin, Jaakko K; Te Moller, Nikae Cr; Mohammadi, Ali; Prakash, Mithilesh; Torniainen, Jari; Brommer, Harold; Nippolainen, Ervin; Shaikh, Rubina; Mäkelä, Janne Ta; Korhonen, Rami K; René van Weeren, P; Afara, Isaac O; Töyräs, Juha (Elsevier BV, 2021)
      Objective To assess the potential of near-infrared spectroscopy (NIRS) for in vivo arthroscopic monitoring of cartilage defects. Method Sharp and blunt cartilage grooves were induced in the radiocarpal and intercarpal ...
      Tieteelliset aikakauslehtiartikkelit

    • Predicting Intelligence Based on Cortical WM/GM Contrast, Cortical Thickness and Volumetry 

      Valverde, JM; Imani, V; Lewis, JD; Tohka, J (Springer International Publishing, 2019)
      We propose a four-layer fully-connected neural network (FNN) for predicting fluid intelligence scores from T1-weighted MR images for the ABCD-challenge. In addition to the volumes of brain structures, the FNN uses cortical ...
      Artikkelit tieteellisissä kokoomateoksissa