Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 5 of 5 for “"Multi-class segmentation"”.
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Multi-class segmentation of brain tumor using Convolution Neural Network
… Network (CNN) architecture is used to segment multi-modal Brain Tumors from Magnetic Resonance (MR) images. Due to the challenges in manual segmentation, computerized brain tumor segmentation is one of the most important challenges in medical imaging. The fully convolutional structure of the …
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Multi-Class 3D Segmentation of Progressive Damage in Advanced Composites using Deep Learning
… is normally analyzed by manual or semi-automated segmentation techniques, where a user identifies and segments the different damage modes (e.g., internal cracks) in the 2D tomograms that comprise the 3D tomographic scan. However, these scans contain a large amount of data (≈10 GB/mm3 ) making it …
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Deep Learning of Semantic Image Labels on HDR Imagery in a Maritime Environment
… the technology. This study creates labels for a multi-class semantic segmentation process, and performs well on water and horizon identification in the littoral zone. Additionally, this work contributes proof that water can be reasonably identified using HDR imagery with semantic networks, which …
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Fully Automated Segmentation of High Grade Serous Ovarian Cancer on Computed Tomography Images using Deep Learning
… networks can be used for the fully automated segmentation of high grade serous ovarian cancer. The recent rise of deep learning has pushed the limits of what algorithms can achieve in fields of image analysis, such as the task of segmentation. The field of medical image segmentation has …
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Advanced Radar Sounder Data Analysis Methods under Limited labeled Data Constraints
… To address this challenge, several automatic classification methods have been developed. However, most of them depend on large labeled datasets and work only when trained and tested on data from the same campaign, which limits their ability to generalize to new environments. This creates the …