Sökning: "Medicinsk bildsegmentering"

Hittade 5 avhandlingar innehållade orden Medicinsk bildsegmentering.

1. 1. Methods for the analysis and characterization of brain morphology from MRI images

   Författare :Irene Brusini; Chunliang Wang; Örjan Smedby; Eric Westman; Lars-Olof Wahlund; Jorge Cardoso; KTH; Karolinska Institutet; Karolinska Institutet; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; Brain MRI; Image Segmentation; Machine Learning; Deep Learning; Shape Analysis; Aging; Neurodegeneration; MRT av hjärnan; Bildsegmentering; Maskininlärning; Djupinlärning; Formanalys; Åldrande; Neurodegeneration; Medical Technology; Medicinsk teknologi;

   Sammanfattning : Brain magnetic resonance imaging (MRI) is an imaging modality that produces detailed images of the brain without using any ionizing radiation. From a structural MRI scan, it is possible to extract morphological properties of different brain regions, such as their volume and shape. LÄS MER

3. 2. Resource efficient automatic segmentation of medical images

   Författare :Minh Hoang Vu; Tommy Löfstedt; Tufve Nyholm; Anders Garpebring; Joakim Jonsson; Örjan Smedby; Umeå universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; radiotherapy; medical imaging; deep learning; convolutional neural network; generative adversarial network; data augmentation; semantic segmentation; classification; activation map compression; radiofysik; radiation physics;

   Sammanfattning : Cancer is one of the leading causes of death worldwide. In 2020, there were around 10 million cancer deaths and nearly 20 million new cancer cases in the world. Radiation therapy is essential in cancer treatments because half of the cancer patients receive radiation therapy at some point. LÄS MER

4. 3. On Medical Image Segmentation With Noisy Labels

   Författare :Marcus Nordström; Henrik Hult; Atsuto Maki; Fredrik Kahl; KTH; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Medical image segmentation; image segmentation; machine learning; supervised learning; label noise; Sörensen-Dice coefficient; soft-Dice; Medicinsk bildsegmentering; bildsegmentering; maskininlärning; övervakat lärande; annoteringsbrus; Sörensen-Dice koefficienten; soft-Dice; Tillämpad matematik och beräkningsmatematik; Applied and Computational Mathematics; Mathematical Statistics; Matematisk statistik;

   Sammanfattning : It is well known that data sets used for training and testing automatic medical image segmentation methods often contain a lot of label noise. Such noise affects the performance of the methods and has been subject to a lot of research. LÄS MER

5. 4. Automatic Virus Identification using TEM : Image Segmentation and Texture Analysis

   Författare :Gustaf Kylberg; Ida-Maria Sintorn; Gunilla Borgefors; Walter Kropatsch; Uppsala universitet; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; image analysis; image processing; virus identification; transmission electron microscopy; texture analysis; texture descriptors; Computerized Image Processing; Datoriserad bildbehandling;

   Sammanfattning : Viruses and their morphology have been detected and studied with electron microscopy (EM) since the end of the 1930s. The technique has been vital for the discovery of new viruses and in establishing the virus taxonomy. Today, electron microscopy is an important technique in clinical diagnostics. LÄS MER

7. 5. Towards Scalable Machine Learning with Privacy Protection

   Författare :Dominik Fay; Mikael Johansson; Tobias J. Oechtering; Jens Sjölund; Antti Honkela; KTH; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Machine Learning; Privacy; Differential Privacy; Dimensionality Reduction; Image Segmentation; Hyperparameter Selection; Adaptive Optimization; Privacy Amplification; Importance Sampling; Maskininlärning; Dataskydd; Differentiell Integritet; Dimensionsreducering; Bildsegmentering; Hyperparameterurval; Adaptiv Optimering; Integritetsförstärkning; Importance Sampling; Datalogi; Computer Science; Informations- och kommunikationsteknik; Information and Communication Technology;

   Sammanfattning : The increasing size and complexity of datasets have accelerated the development of machine learning models and exposed the need for more scalable solutions. This thesis explores challenges associated with large-scale machine learning under data privacy constraints. LÄS MER