Sökning: "Convolutional Neural Networks"

Visar resultat 1 - 5 av 72 avhandlingar innehållade orden Convolutional Neural Networks.

1. 1. G-equivariant convolutional neural networks

   Författare :Jimmy Aronsson; Chalmers tekniska högskola; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; deep learning; induced representations; homogeneous vector bundles; convolutional neural networks; homogeneous spaces; symmetry;

   Sammanfattning : Over the past decade, deep learning has revolutionized industry and academic research. Neural networks have been used to solve a multitude of previously unsolved problems and to significantly improve the state-of-the-art on other tasks, in some cases reaching superhuman levels of performance. LÄS MER

3. 2. Deep Neural Networks and Image Analysis for Quantitative Microscopy

   Författare :Sajith Kecheril Sadanandan; Carolina Wählby; Petter Ranefall; Ewert Bengtsson; Jeroen van der Laak; Uppsala universitet; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Deep neural networks; convolutional neural networks; image analysis; quantitative microscopy; bright-field microscopy; Datoriserad bildbehandling; Computerized Image Processing;

   Sammanfattning : Understanding biology paves the way for discovering drugs targeting deadly diseases like cancer, and microscopy imaging is one of the most informative ways to study biology. However, analysis of large numbers of samples is often required to draw statistically verifiable conclusions. LÄS MER

4. 3. Mathematical Foundations of Equivariant Neural Networks

   Författare :Jimmy Aronsson; Chalmers tekniska högskola; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; symmetry; geometric deep learning; gauge theory; induced representations; fiber bundles; convolutional neural networks; equivariance;

   Sammanfattning : Deep learning has revolutionized industry and academic research. Over the past decade, neural networks have been used to solve a multitude of previously unsolved problems and to significantly improve the state of the art on other tasks. However, training a neural network typically requires large amounts of data and computational resources. LÄS MER

5. 4. Machine Learning Methods for Image Analysis in Medical Applications, from Alzheimer's Disease, Brain Tumors, to Assisted Living

   Författare :Chenjie Ge; Chalmers tekniska högskola; []
   Nyckelord :MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; convolutional neural networks; Alzheimer s disease detection; machine learning; deep learning; fall detection; glioma subtype classification; generative adversarial networks; recurrent convolutional networks; spiking neural networks; visual prosthesis; semi-supervised learning;

   Sammanfattning : Healthcare has progressed greatly nowadays owing to technological advances, where machine learning plays an important role in processing and analyzing a large amount of medical data. This thesis investigates four healthcare-related issues (Alzheimer's disease detection, glioma classification, human fall detection, and obstacle avoidance in prosthetic vision), where the underlying methodologies are associated with machine learning and computer vision. LÄS MER

7. 5. Water–fat separation in magnetic resonance imaging and its application in studies of brown adipose tissue

   Författare :Jonathan Andersson; Joel Kullberg; Håkan Ahlström; Mark Lubberink; Kerstin Lagerstrand; Uppsala universitet; []
   Nyckelord :MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; brown adipose tissue; magnetic resonance imaging; water–fat signal separation; graph-cut; positron emission tomography; 18F-fludeoxyglucose; infrared thermography; machine learning; artificial neural networks; deep learning; convolutional neural networks; Radiology; Radiologi;

   Sammanfattning : Virtually all the magnetic resonance imaging (MRI) signal of a human originates from water and fat molecules. By utilizing the property chemical shift the signal can be separated, creating water- and fat-only images. LÄS MER