Essays about: "3D Medicinsk bildsegmentering"
Found 5 essays containing the words 3D Medicinsk bildsegmentering.
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1. Self-learning for 3D segmentation of medical images from single and few-slice annotation
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)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
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2. Self-supervised pre-training of an attention-based model for 3D medical image segmentation
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)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
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3. Uncertainty Estimation in Volumetric Image Segmentation
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)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
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4. Data Augmentation to Improve Cross-Domain Generalization in Deep Learning MRI Segmentation
University essay from Lunds universitet/Matematik LTHAbstract : Semantic segmentation of medical images is an important task with many applications. However, manually delineating 3D images is time-consuming and the demand for automation is high. For many image segmentation tasks, deep learning has provided state-of-the-art results. READ MORE
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5. Medical Image Segmentation using Attention-Based Deep Neural Networks
University essay from KTH/Medicinsk avbildningAbstract : During the last few years, segmentation architectures based on deep learning achieved promising results. On the other hand, attention networks have been invented years back and used in different tasks but rarely used in medical applications. READ MORE
