Sökning: "Jens Sjölund"

Hittade 3 avhandlingar innehållade orden Jens Sjölund.

  1. 1. Algorithms for magnetic resonance imaging in radiotherapy

    Författare :Jens Sjölund; Hans Knutsson; Anders Eklund; Mats Andersson; Evren Özarslan; Aasa Feragen; Linköpings universitet; []
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY;

    Sammanfattning : Radiotherapy plays an increasingly important role in cancer treatment, and medical imaging plays an increasingly important role in radiotherapy. Magnetic resonance imaging (MRI) is poised to be a major component in the development towards more effective radiotherapy treatments with fewer side effects. LÄS MER

  3. 2. MRI based radiotherapy planning and pulse sequence optimization

    Författare :Jens Sjölund; Hans Knutsson; Mats Andersson; Michael Felsberg; Linköpings universitet; []
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY;

    Sammanfattning : Radiotherapy plays an increasingly important role in cancer treatment, and medical imaging plays an increasingly important role in radiotherapy. Magnetic resonance imaging (MRI) is poised to be a major component in the development towards more effective radiotherapy treatments with fewer side effects. LÄS MER

  4. 3. Towards Scalable Machine Learning with Privacy Protection

    Författare :Dominik Fay; Mikael Johansson; Tobias J. Oechtering; Jens Sjölund; Antti Honkela; KTH; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Machine Learning; Privacy; Differential Privacy; Dimensionality Reduction; Image Segmentation; Hyperparameter Selection; Adaptive Optimization; Privacy Amplification; Importance Sampling; Maskininlärning; Dataskydd; Differentiell Integritet; Dimensionsreducering; Bildsegmentering; Hyperparameterurval; Adaptiv Optimering; Integritetsförstärkning; Importance Sampling; Datalogi; Computer Science; Informations- och kommunikationsteknik; Information and Communication Technology;

    Sammanfattning : The increasing size and complexity of datasets have accelerated the development of machine learning models and exposed the need for more scalable solutions. This thesis explores challenges associated with large-scale machine learning under data privacy constraints. LÄS MER