Sökning: "Address Segmentation"

Visar resultat 1 - 5 av 40 uppsatser innehållade orden Address Segmentation.

1. 1. Domain Adaptation for Multi-Contrast Image Segmentation in Cardiac Magnetic Resonance Imaging

   Master-uppsats, KTH/Skolan för kemi, bioteknologi och hälsa (CBH)

   Författare :Thomas Proudhon; [2023]
   Nyckelord :Cardiac Magnetic Resonance Imaging; Deep Learning; Domain Adaptation; Unsupervised Segmentation; Image-to-image Translation;

   Sammanfattning : Accurate segmentation of the ventricles and myocardium on Cardiac Magnetic Resonance (CMR) images is crucial to assess the functioning of the heart or to diagnose patients suffering from myocardial infarction. However, the domain shift existing between the multiple sequences of CMR data prevents a deep learning model trained on a specific contrast to be used on a different sequence. LÄS MER

3. 2. Improved U-Net architecture for Crack Detection in Sand Moulds

   Kandidat-uppsats, Högskolan i Gävle/Datavetenskap

   Författare :Husain Ahmed; Hozan Bajo; [2023]
   Nyckelord :U-Net Architecture; Semantic Segmentation; Convolutional Neural Networks; Crack Detection;

   Sammanfattning : The detection of cracks in sand moulds has long been a challenge for both safety and maintenance purposes. Traditional image processing techniques have been employed to identify and quantify these defects but have often proven to be inefficient, labour-intensive, and time-consuming. LÄS MER

4. 3. Self-learning for 3D segmentation of medical images from single and few-slice annotation

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Côme Lassarat; [2023]
   Nyckelord :Self-supervised Learning; Segmentation; Medical images; Självövervakad inlärning; segmentering; medicinska bilder;

   Sammanfattning : Training deep-learning networks to segment a particular region of interest (ROI) in 3D medical acquisitions (also called volumes) usually requires annotating a lot of data upstream because of the predominant fully supervised nature of the existing stateof-the-art models. To alleviate this annotation burden for medical experts and the associated cost, leveraging self-learning models, whose strength lies in their ability to be trained with unlabeled data, is a natural and straightforward approach. LÄS MER

5. 4. EXTRACTING REGIONS OF INTEREST AND DETECTING OUTLIERS FROM IMAGE DATA

   Kandidat-uppsats, Mälardalens universitet/Akademin för innovation, design och teknik

   Författare :Jessica Ström; Erik Backhans; [2023]
   Nyckelord :Artificial Intelligence AI ; Machine Vision; Outlier Detection; Autoencoders; Neural Networks; Region Of Interest ROI ;

   Sammanfattning : Volvo Construction Equipment (CE) are facing the challenge of vibrations in their wheel loaders that generate disruptive noise and impact the driver's experience. These vibrations have been linked to the contact surface between the crown wheel and pinion gear in the vehicles drive-axles. LÄS MER

7. 5. Aggregating predictions of a yeast semantic segmentation model : Reducing a pixel classifier into a binary image classifier

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Ali Muquri; [2023]
   Nyckelord :;

   Sammanfattning : The introduction of machine learning in clinical microbiology is important for aiding clinical laboratories with highly repetitive tasks that are fatiguing, error-prone, and require long employee training time due to the complex nature of the task. A challenging task that belongs to the subareas that need assistance is yeast detection in fluorescence microscopy where various yeast morphologies exist. LÄS MER