Sökning: "Self Segmentation"

Visar resultat 1 - 5 av 47 uppsatser innehållade orden Self Segmentation.

  1. 1. Exploring adaptation of self-supervised representation learning to histopathology images for liver cancer detection

    Uppsats för yrkesexamina på avancerad nivå, Luleå tekniska universitet/Institutionen för system- och rymdteknik

    Författare :Markus Jonsson; [2024]
    Nyckelord :Self-supervised learning; Representation learning; Computer vision;

    Sammanfattning : This thesis explores adapting self-supervised representation learning to visual domains beyond natural scenes, focusing on medical imaging. The research addresses the central question: "How can self-supervised representation learning be specifically adapted for detecting liver cancer in histopathology images?" The study utilizes the PAIP 2019 dataset for liver cancer segmentation and employs a self-supervised approach based on the VICReg method. LÄS MER

  3. 2. 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

  4. 3. Semi-automatic Segmentation & Alignment of Handwritten Historical Text Images with the use of Bayesian Optimisation

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

    Författare :Philip MacCormack; [2023]
    Nyckelord :handwritten text recognition; machine learning; bayesian optimisation; image analysis; segmentation; alignment;

    Sammanfattning : To effortlessly digitise historical documents has risen to be of great interest for some time. Part of the digitisation is what is called annotating of the data. Such data annotations are obtained in a process called alignment which links words in an image to the transcript. LÄS MER

  5. 4. Improving Change Point Detection Using Self-Supervised VAEs : A Study on Distance Metrics and Hyperparameters in Time Series Analysis

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

    Författare :Daniel Workinn; [2023]
    Nyckelord :Change point detection; Time series data; Segmentation; Machine learning; Data mining; Detektion av brytpunkter; Tidsseriedata; Segmentering; Maskininlärning; Datautvinning;

    Sammanfattning : This thesis addresses the optimization of the Variational Autoencoder-based Change Point Detection (VAE-CP) approach in time series analysis, a vital component in data-driven decision making. We evaluate the impact of various distance metrics and hyperparameters on the model’s performance using a systematic exploration and robustness testing on diverse real-world datasets. LÄS MER

  7. 5. 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