Sökning: "Gleason grading"

Visar resultat 1 - 5 av 8 uppsatser innehållade orden Gleason grading.

  1. 1. Towards non-invasive Gleason grading of prostate cancer using diffusion weighted MRI

    Master-uppsats, Umeå universitet/Institutionen för fysik

    Författare :Pierre Hillergren; [2020]
    Nyckelord :;

    Sammanfattning : Prostate cancer is one of the most common cancer diagnosis in men. This project aimed to help in characterization and treatment planning of prostate cancer by producing a Gleason grading probability based on apparent diffusion coefficient (ADC). LÄS MER

  3. 2. Classification of High Risk Prostate Cancer using Deep Learning

    Master-uppsats, Lunds universitet/Matematik LTH

    Författare :Elin Olofsson; [2019]
    Nyckelord :Prostate Cancer; CNN; Deep Learning; PRIAS; Active Surveillance; Digital Pathology; Mathematics and Statistics;

    Sammanfattning : Prostate cancer is one of the most common types of cancer for men, making proper diagnostic essential. Using machine learning as a tool to help in digital pathology has become increasingly popular and helps to limit the high intra observer variability between pathologists. LÄS MER

  4. 3. Generative Adversarial Networks to enhance decision support in digital pathology

    Master-uppsats, Linköpings universitet/Statistik och maskininlärning

    Författare :Alessia De Biase; [2019]
    Nyckelord :Generative Adversarial Networks; Digital Pathology; CycleGAN; Style Transfer;

    Sammanfattning : Histopathological evaluation and Gleason grading on Hematoxylin and Eosin(H&E) stained specimens is the clinical standard in grading prostate cancer. Recently, deep learning models have been trained to assist pathologists in detecting prostate cancer. LÄS MER

  5. 4. Automatic Gleason Classification of Prostate Cancer - Classification of Small Regions

    Master-uppsats, Lunds universitet/Matematik LTH

    Författare :Kasper Tall; [2018]
    Nyckelord :Prostate cancer; Gleason grading; CNN; Inception; ResNet; Inception-ResNet; Technology and Engineering;

    Sammanfattning : Purpose: To classify the severity of a case of prostate cancer, physicians use the 10-grade Gleason score. The purpose of this Master’s thesis is to study how small dimensions of image crops affect the Gleason 5-classification capability of a machine learning system. LÄS MER

  7. 5. Rotational Invariant Convolutional Neural Networks for Prostate Cancer Classification

    Master-uppsats, Lunds universitet/Matematik LTH

    Författare :Joel Ekelund; [2017]
    Nyckelord :Convolutional Neural Networks; rotation invariance; deep learning; automated Gleason grading; Mathematics and Statistics;

    Sammanfattning : Prostate cancer was in 2012 the second most common type of cancer for males globally. To be able to treat prostate cancer in the most effective way it is important to know how aggressive the cancer is. This aggressiveness is graded using the Gleason score. LÄS MER