Sökning: "convolutional autoencoder"

Visar resultat 1 - 5 av 7 avhandlingar innehållade orden convolutional autoencoder.

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

   Författare :Muhaddisa Barat Ali; Chalmers tekniska högskola; []
   Nyckelord :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;

   Sammanfattning : 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. LÄS MER

3. 2. 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

4. 3. Supervised and Unsupervised Deep Learning Models for Flood Detection

   Författare :Ritu Yadav; Yifang Ban; Andrea Nascetti; Nicolas Audebert; KTH; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; Floods; Remote Sensing; Sentinel-1 SAR; Segmentation; Change Detection; DEM; Data Fusion; Time Series; Deep Learning; Unsupervised Learning; Contrastive Learning; Self-Attention; Convolutional LSTM; Variational AutoEncoder VAE ; Geoinformatik; Geoinformatics;

   Sammanfattning : Human civilization has an increasingly powerful influence on the earthsystem. Affected by climate change and land-use change, floods are occurringacross the globe and are expected to increase in the coming years. Currentsituations urge more focus on efficient monitoring of floods and detecting impactedareas. LÄS MER

5. 4. Towards safe and efficient application of deep neural networks in resource-constrained real-time embedded systems

   Författare :Siyu Luan; Zonghua Gu; Leonid B. Freidovich; Lei Feng; Umeå universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Machine Learning Deep Learning; Real-Time Embedded systems; Out-of-Distribution Detection; Distribution Shifts; Deep Reinforcement Learning; Model Compression; Policy Distillation.;

   Sammanfattning : We consider real-time safety-critical systems that feature closed-loop interactions between the embedded computing system and the physical environment with a sense-compute-actuate feedback loop. Deep Learning (DL) with Deep Neural Networks (DNNs) has achieved success in many application domains, but there are still significant challenges in its application in real-time safety-critical systems that require high levels of safety certification under significant hardware resource constraints. LÄS MER

7. 5. Principles of regional covariance in brain structure

   Författare :Lars Forsberg; Karolinska Institutet; Karolinska Institutet; []
   Nyckelord :;

   Sammanfattning : The human brain varies between individuals in both shape and size. These variations are not unique for each brain region. This causes grey matter density between regions to covary, a phenomenon known as “structural brain covariance”. LÄS MER