Sökning: "Deep approaches to learning"

Visar resultat 11 - 15 av 109 avhandlingar innehållade orden Deep approaches to learning.

1. 11. Adapting Deep Learning for Microscopy: Interaction, Application, and Validation

   Författare :Ankit Gupta; Carolina Wählby; Ida-Maria Sintorn; Ola Spjuth; Andreas Hellander; Philip Kollmannsberger; Uppsala universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Deep Learning; Microscopy; Human-in-the-Loop; Semi-Supervised Learning; Application-Specific Analysis; Image Classification; Image-to-Image Translation; Template Matching; Computerized Image Processing; Datoriserad bildbehandling;

   Sammanfattning : Microscopy is an integral technique in biology to study the fundamental components of life visually. Digital microscopy and automation have enabled biologists to conduct faster and larger-scale experiments with a sharp increase in the data generated. LÄS MER

3. 12. 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

4. 13. Deep Learning Applications for Autonomous Driving

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

   Sammanfattning : 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. LÄS MER

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

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

   Sammanfattning : 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. LÄS MER

7. 15. Image Analysis and Deep Learning for Applications in Microscopy

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

   Sammanfattning : 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. LÄS MER