Sökning: "Convolutional Neural Networks"

Visar resultat 11 - 15 av 72 avhandlingar innehållade orden Convolutional Neural Networks.

1. 11. Deep Learning Methods for Classification of Glioma and its Molecular Subtypes

   Författare :Muhaddisa Barat Ali; Chalmers tekniska högskola; []
   Nyckelord :MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; 1p 19q codeletion; generative adversarial network; convolutional neural network; glioma subtype classification; IDH mutation.; Deep learning; cycleGAN; convolutional autoencoder;

   Sammanfattning : Diagnosis and timely treatment play an important role in preventing brain tumor growth. Clinicians are unable to reliably predict LGG molecular subtypes from magnetic resonance imaging (MRI) without taking biopsy. Accurate diagnosis prior to surgery would be important. LÄS MER

3. 12. Deep Regression and Segmentation for Medical Inference from Large-Scale Magnetic Resonance Imaging

   Författare :Taro Langner; Joel Kullberg; Anders Eklund; Uppsala universitet; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Magnetic resonance imaging; medical image analysis; neural networks; machine learning; semantic segmentation; deep regression; saliency analysis; uncertainty quantification; UK Biobank; Medical Informatics; Medicinsk informatik;

   Sammanfattning : Large-scale studies, such as UK Biobank, acquire medical imaging data for thousands of participants. With magnetic resonance imaging (MRI), comprehensive representations of human anatomy can be provided for non-invasive assessments of health-related conditions, body composition, organ volumes, and more. LÄS MER

4. 13. Realistic Real-Time Rendering of Global Illumination and Hair through Machine Learning Precomputations

   Författare :Roc Ramon Currius; Chalmers tekniska högskola; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Neural Networks; Real-time rendering; Lightfields; Realistic Rendering; Global Illumination; Hair Rendering; Machine Learning;

   Sammanfattning : Over the last decade, machine learning has gained a lot of traction in many areas, and with the advent of new GPU models that include acceleration hardware for neural network inference, real-time applications have also started to take advantage of these algorithms. In general, machine learning and neural network methods are not designed to run at the speeds that are required for rendering in high-performance real-time environments, except for very specific and typically limited uses. LÄS MER

5. 14. Modeling Music : Studies of Music Transcription, Music Perception and Music Production

   Författare :Anders Elowsson; Anders Friberg; Pawel Herman; Anders Askenfelt; Gerhard Widmer; KTH; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Music Information Retrieval; MIR; Music; Music Transcription; Music Perception; Music Production; Tempo Estimation; Beat Tracking; Polyphonic Pitch Tracking; Polyphonic Transcription; Music Speed; Music Dynamics; Long-time average spectrum; LTAS; Algorithmic Composition; Deep Layered Learning; Convolutional Neural Networks; Rhythm Tracking; Ensemble Learning; Perceptual Features; Representation Learning;

   Sammanfattning : This dissertation presents ten studies focusing on three important subfields of music information retrieval (MIR): music transcription (Part A), music perception (Part B), and music production (Part C).In Part A, systems capable of transcribing rhythm and polyphonic pitch are described. LÄS MER

7. 15. Deep Learning Applications - From image analysis to medical diagnosis

   Författare :Saga Helgadottir; Göteborgs universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; deep learning; neural networks; image analysis; microscopy; medical diagnosis;

   Sammanfattning : Deep learning is a subcategory of machine learning and artificial intelligence. Instead of using explicit rules to perform a desired task as in standard algorithmic approaches, machine-learning algorithms autonomously learn from data to determine the rules for the task at hand. LÄS MER