Blar i Institutt for datateknologi og informatikk på forfatter "Adde, Lars"

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    • Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk 

      Groos, Daniel; Adde, Lars; Aubert, Sindre Aarnes; Boswell, Lynn; De Regnier, Raye-Ann; Fjørtoft, Toril Larsson; Gaebler-Spira, Deborah; Haukeland, Andreas; Loennecken, Marianne; Msall, Michael; Moinichen, Unn Inger; Pascal, Aurelie; Peyton, Colleen; Ramampiaro, Heri; Schreiber, Michael D.; Silberg, Inger Elisabeth; Songstad, Nils Thomas; Thomas, Niranjan; van den Broeck, Christine; Øberg, Gunn Kristin; Ihlen, Espen Alexander F.; Støen, Ragnhild (Peer reviewed; Journal article, 2022)
      Importance Early identification of cerebral palsy (CP) is important for early intervention, yet expert-based assessments do not permit widespread use, and conventional machine learning alternatives lack validity. Objective ...

    • Infant Body Part Tracking in Videos Using Deep Learning - Facilitating Early Detection of Cerebral Palsy 

      Groos, Daniel; Aurlien, Kristian (Master thesis, 2018)
      The breakthrough of Artificial Intelligence with the advent of Deep Learning has opened paths beyond what have earlier been explored. Within the medical domain, there are potentials to improve how problems are addressed ...

    • Skeleton Based Cerebral Palsy Diagnosis using Deep Learning and Attention 

      Vold, Martin (Master thesis, 2020)
      Dyp læring har i de siste årene oppnådd gode resultater innen forskningsfelt som datasyn og menneskelig aktivitets gjenkjenning. Innen medisin har disse gjennombruddene åpnet nye dører for hvordan problemer blir løst og ...

    • Towards human-level performance on automatic pose estimation of infant spontaneous movements 

      Groos, Daniel; Adde, Lars; Støen, Ragnhild; Ramampiaro, Heri; Ihlen, Espen Alexander F. (Peer reviewed; Journal article, 2021)
      Assessment of spontaneous movements can predict the long-term developmental disorders in high-risk infants. In order to develop algorithms for automated prediction of later disorders, highly precise localization of segments ...