Sökning: "faltningsnätverk"

Visar resultat 11 - 15 av 81 uppsatser innehållade ordet faltningsnätverk.

  1. 11. Breast Cancer Histological Grading Using Graph Convolutional Networks

    Master-uppsats, KTH/Matematik (Avd.)

    Författare :Anton Normelius; [2022]
    Nyckelord :GCN; GraphSAGE; Breast cancer histological grading; GCN; GraphSAGE; Histologisk gradering av bröstcancer;

    Sammanfattning : Technological advancements have opened up the possibility of digitizing the pathological landscape, enabling deep learning-based methods to analyze digitized tissue samples, i.e., whole slide images (WSIs). Attention has recently shifted toward modeling WSIs as graphs since graph representations can capture dynamic relationships. LÄS MER

  3. 12. Unsupervised Image Classification Using Domain Adaptation : Via the Second Order Statistic

    Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Avdelningen för systemteknik

    Författare :Joel Bjervig; [2022]
    Nyckelord :machine learning; deep learning; image classification; transfer learning; domain adaptation; unsupervised domain adaptation; covariance;

    Sammanfattning : Framgången inom maskininlärning och djupinlärning beror till stor del på stora, annoterade dataset. Att tilldela etiketter till data är väldigt resurskrävande och kan till viss del undvikas genom att utnyttja datans statistiska egenskaper. LÄS MER

  4. 13. A study of the biological sex in the classification of Alzheimer’s disease using a convolutional neural network

    Kandidat-uppsats, KTH/Datavetenskap

    Författare :Emilia Rieschel; Emm Nilsson; [2022]
    Nyckelord :;

    Sammanfattning : This report investigates whether Alzheimer’s MRI scans could be classified more accurately using deep learning if the biological sex was considered. The data used in the study were female and male Alzheimer’s Disease (AD) and Cognitive Normal (CN) MRI scans. The data was divided into a training and test set. LÄS MER

  5. 14. Labelling Motion Capture Markers Using Dynamic Graph Convolutional Neural Networks

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Jacob Stuart; [2022]
    Nyckelord :;

    Sammanfattning : This thesis concerns labelling unlabelled motion capture (mocap) data using a Dynamic Graph Convolutional Neural Network (DGCNN) [46]. The most common type of motion-capture system, i.e. passive motion-capture, records the 3D position of multiple reflective markers using multiple infrared cameras with overlapping fields of view. LÄS MER

  7. 15. The Impact of Noise on Generative and Discriminative Image Classifiers

    Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Maximilian Stenlund; Valdemar Jakobsson; [2022]
    Nyckelord :Artificial intelligence; Adversarial noise; Discriminative; Generative; Salt and Pepper noise; Gaussian noise; neural networks; Normalized flows; Convolutional networks;

    Sammanfattning : This report analyzes the difference between discriminative and generative image classifiers when tested on noise. The generative classifier was a maximum-likelihood based classifier using a normalizing flow as the generative model. In this work, a coupling flow such as RealNVP was used. LÄS MER