Essays about: "medicinska bilder"

Showing result 1 - 5 of 46 essays containing the words medicinska bilder.

  1. 1. Evaluation of different block-copolymer coatings of iron oxide nanoparticles by flash nanoprecipitation

    University essay from KTH/Fiber- och polymerteknologi

    Author : Felix Bogdan; [2023]
    Keywords : Nanopartiklar; organisk katalysator; ROP; block-sampolymerer PL G A; oljesyra IONPs; Nanoparticles; organic catalyst; ROP; block-copolymers PL G A; oleic acid IONPs;

    Abstract : Nanopartiklar (NP) erbjuder unika möjligheter för medicinska tillämpningar, inklusive kontrollerad frisättning av cancerläkemedel, användning som bildkontrast vid avbildningsprocedurer eller hypertermisk behandling av cancerceller. Flash nanoprecipitation (FNP) producerar NPs för att kombinera dessa tillämpningar i en snabb, billig och skalbar beläggningsprocess. READ MORE

  3. 2. Deep Learning-based Regularizers for Cone Beam Computed Tomography Reconstruction

    University essay from KTH/Matematisk statistik

    Author : Sabina Syed; Josefin Stenberg; [2023]
    Keywords : Adversarial Convex Regularization; Computer Vision; Cone Beam Computed Tomography; Convolutional Neural Networks; Deep Learning; Image Reconstruction; Adversarial Convex Regularization; Bildrekonstruktion; Datorseende; Djupinlärning; Faltningsnätverk; Volymtomografi;

    Abstract : Cone Beam Computed Tomography is a technology to visualize the 3D interior anatomy of a patient. It is important for image-guided radiation therapy in cancer treatment. During a scan, iterative methods are often used for the image reconstruction step. READ MORE

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

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

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

    Abstract : 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. READ MORE

  5. 4. Self-supervised pre-training of an attention-based model for 3D medical image segmentation

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Albert Sund Aillet; [2023]
    Keywords : Computer vision; Deep learning; 3D Medical image segmentation; Self-supervised learning; Datorseende; Djupinlärning; 3D Medicinsk bildsegmentering; Självövervakad träning;

    Abstract : Accurate segmentation of anatomical structures is crucial for radiation therapy in cancer treatment. Deep learning methods have been demonstrated effective for segmentation of 3D medical images, establishing the current standard. However, they require large amounts of labelled data and suffer from reduced performance on domain shift. READ MORE

  6. 5. Uncertainty Estimation in Volumetric Image Segmentation

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Donggyun Park; [2023]
    Keywords : Uncertainty Estimation; Uncertainty Quantification UQ ; Volumetric Image Segmentation; 3D U-Net; test-time data augmentation; Deep ensemble;

    Abstract : The performance of deep neural networks and estimations of their robustness has been rapidly developed. In contrast, despite the broad usage of deep convolutional neural networks (CNNs)[1] for medical image segmentation, research on their uncertainty estimations is being far less conducted. READ MORE