Search for dissertations about: "Deep Learning"

Showing result 21 - 25 of 360 swedish dissertations containing the words Deep Learning.

2. 21. Deep Learning Methods for Classification of Gliomas and Their Molecular Subtypes, From Central Learning to Federated Learning

   Author : Muhaddisa Barat Ali; Chalmers tekniska högskola; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; glioma subtype classification; convolutional autoencoder; convolutional NN; multi-stream U-Net.; CycleGAN; 1p 19q codeletion; federated learning; IDH mutation; generative adversarial network; Deep learning;

   Abstract : The most common type of brain cancer in adults are gliomas. Under the updated 2016 World Health Organization (WHO) tumor classification in central nervous system (CNS), identification of molecular subtypes of gliomas is important. READ MORE

4. 22. Deep Learning Applications for Autonomous Driving

   Author : Luca Caltagirone; Chalmers tekniska högskola; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; sensor fusion; computer vision; deep learning; autonomous driving; robotic perception and planning;

   Abstract : This thesis investigates the usefulness of deep learning methods for solving two important tasks in the field of driving automation: (i) Road detection, and (ii) driving path generation. Road detection was approached using two strategies: The first one considered a bird's-eye view of the driving scene obtained from LIDAR data, whereas the second carried out camera-LIDAR fusion in the camera perspective. READ MORE

5. 23. Deep Learning for Digital Pathology in Limited Data Scenarios

   Author : Karin Stacke; Jonas Unger; Gabriel Eilertsen; Claes Lundström; Henning Müller; Linköpings universitet; []
   Keywords : MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; Medical imaging; Digital pathology; Radiology; Machine learning; Deep learning.;

   Abstract : The impressive technical advances seen for machine learning algorithms in combination with the digitalization of medical images in the radiology and pathology departments show great promise in introducing powerful image analysis tools for image diagnostics. In particular, deep learning, a subfield within machine learning, has shown great success, advancing fields such as image classification and detection. READ MORE

6. 24. Image Analysis and Deep Learning for Applications in Microscopy

   Author : Omer Ishaq; Carolina Wählby; Bernd Rieger; Uppsala universitet; []
   Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Machine learning; Deep learning; Image analysis; Quantitative microscopy; Bioimaging; Computerized Image Processing; Datoriserad bildbehandling;

   Abstract : Quantitative microscopy deals with the extraction of quantitative measurements from samples observed under a microscope. Recent developments in microscopy systems, sample preparation and handling techniques have enabled high throughput biological experiments resulting in large amounts of image data, at biological scales ranging from subcellular structures such as fluorescently tagged nucleic acid sequences to whole organisms such as zebrafish embryos. READ MORE

7. 25. Multispectral Remote Sensing and Deep Learning for Wildfire Detection

   Author : Xikun Hu; Yifang Ban; Ioannis Gitas; KTH; []
   Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; active fire detection; biome; multi-criteria; Sentinel-2; Landsat-8; burned area mapping; deep learning; semantic segmentation; machine learning.; aktiv branddetektering; biom; multikriterietillvägagångssätt; Sentinel-2; Landsat-8; kartläggning av bränt område; djupinlärning; semantisk segmentering; maskininlärningsmetoderna; Geoinformatik; Geoinformatics;

   Abstract : Remote sensing data has great potential for wildfire detection and monitoring with enhanced spatial resolution and temporal coverage. Earth Observation satellites have been employed to systematically monitor fire activity over large regions in two ways: (i) to detect the location of actively burning spots (during the fire event), and (ii) to map the spatial extent of the burned scars (during or after the event). READ MORE