Sökning: "unsupervised segmentation"

Visar resultat 1 - 5 av 37 uppsatser innehållade orden unsupervised segmentation.

  1. 1. Mathematical modelling simulation data and artificial intelligence for the study of tumour-macrophage interaction

    Magister-uppsats, Högskolan i Skövde/Institutionen för biovetenskap

    Författare :Jaysmita Khanindra Chaliha; [2023]
    Nyckelord :;

    Sammanfattning : The study explores the integration of mathematical modelling and machine learning to understand tumour-macrophage interactions in the tumour microenvironment. It details mathematical models based on biochemistry and physics for predicting tumour dynamics, highlighting the role of macrophages. LÄS MER

  3. 2. 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

  4. 3. Applying Machine Learning Techniques for Anomaly Detection in Wooden Plank Images

    Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Avdelningen Vi3

    Författare :Iza Smedberg; [2023]
    Nyckelord :Anomaly Detection; Machine Vision; Machine Learning; Neural Network;

    Sammanfattning : Anomaly detection is an important first step of quality control in manufacturing processes. In wooden planks, anomalies such as broken knots and resin pockets can lower the quality of the final product. With the help of machine vision, inspections can be made faster, at higher accuracy, and at a lower cost. LÄS MER

  5. 4. Real-time Unsupervised Domain Adaptation

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

    Författare :Marc Botet Colomer; [2023]
    Nyckelord :Unsupervised Domain Adaptation; Real-Time applications; Online Learning; Self-Learning; Semantic Segmentation; Reinforcement Learning; Oövervakad domänanpassning; Realtidsapplikationer; Onlineinlärning; Självinlärning; Semantisk Segmentering; Förstärkningsinlärning;

    Sammanfattning : Machine learning systems have been demonstrated to be highly effective in various fields, such as in vision tasks for autonomous driving. However, the deployment of these systems poses a significant challenge in terms of ensuring their reliability and safety in diverse and dynamic environments. LÄS MER

  7. 5. Semi-Supervised Domain Adaptation for Semantic Segmentation with Consistency Regularization : A learning framework under scarce dense labels

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

    Författare :Daniel Morales Brotons; [2023]
    Nyckelord :Domain Adaptation; Semi-Supervised Learning; Semi-Supervised Domain Adaptation; Semantic Segmentation; Consistency Regularization; Domain Adaptation; Semi-Supervised Learning; Semi-Supervised Domain Adaptation; Semantisk Segmentering; Konsistensregularisering;

    Sammanfattning : Learning from unlabeled data is a topic of critical significance in machine learning, as the large datasets required to train ever-growing models are costly and impractical to annotate. Semi-Supervised Learning (SSL) methods aim to learn from a few labels and a large unlabeled dataset. LÄS MER