Sökning: "glioma subtype classification"

Hittade 4 avhandlingar innehållade orden glioma subtype classification.

  1. 1. 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. 2. 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

  4. 3. 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

  5. 4. Modeling and investigations of high-grade glioma to understand tumorigenesis and heterogeneity

    Författare :Nagaprathyusha Maturi; Lene Uhrbom; Helena Carén; Uppsala universitet; []
    Nyckelord :MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; glioblastoma; diffuse pediatric-type high-grade glioma; cell of origin; heterogeneity; serum culture; glioblastoma stem cell; chromatin accessibility; cross-species conservation; Biology with specialization in Molecular Biology; Biologi med inriktning mot molekylärbiologi;

    Sammanfattning : Glioblastoma (GBM) is the most common primary malignant brain tumor. As per 2021 WHO classification, the corresponding diagnosis in children is called diffuse pediatric-type high-grade glioma (pHGG). In both cases tumors are histone 3 (H3) and isocitrate dehydrogenase 1/2 (IDH1/2) wildtype (wt). LÄS MER