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 7 of 7 for “"deep learning image processing"”.
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Multiparametric Magnetic Resonance Imaging Artificial Intelligence Pipeline For Oropharyngeal Cancer Radiotherapy Treatment Guidance
… that is rising in incidence. Radiographic images are crucial for assessment of OPC and aid in radiotherapy (RT) treatment. However, RT planning with conventional imaging approaches requires operator-dependent tumor segmentation, which is the primary source of treatment error. Further, OPC …
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Towards image registration of dynamic contrast enhanced MRI using deep learning
… involves rapidly acquiring multiple T1-weighted images over a few minutes whilst injecting a contrast agent, which causes a rapid increase in image intensity. Each type of tissue exhibits unique intensity enhancement characteristics over time. Typically, DCE-MRI data undergoes quantitative …
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Deep Learning-Based Comprehensive Pathology Image Analysis
The advances in deep learning during the past decade have provided great tools for the analysis of histopathology images. Deep learning algorithms can aid in the routine diagnostics and have the potential to extract hidden information directly from slide images, providing valuable information for …
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Deep learning for image processing in optical super-resolution microscopy
… is fundamentally governed by a trade-off between image quality, imaging speed and duration. The quality can be considered a function of the signal-to-noise ratio, contrast and image resolution, which are all limited by the amount of light that can be acquired within a set exposure time. Many …
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Improving Cone-Beam Computed Tomography Based Adaptive Radiation Therapy with Deep Learning
… challenge involves generating synthetic CT (sCT) images that retain CBCT anatomy while maintaining CT image quality. Clinically used sCT is typically obtained through deformable image registration (DIR) between pre-planning CT (pCT) and CBCT; however, this method often inadequately preserves CBCT …
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Learning and evaluating image representations
Prior to deep learning it was common to approach computer vision problems as describing a model that could be learned from a relatively small amount of data by incorporating domain knowledge. For example, image prediction tasks such as intrinsic image decomposition were approached by thinking about …
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Bridging Mri Reconstruction Across Eras: From Novel Optimization Of Traditional Methods To Efficient Deep Learning Strategies
… the redundancy among these coils were used for image reconstruction. Following the clinical impact and success of PI methods, compressed sensing (CS) techniques were developed to reconstruct images by using compressibility of images in a pre-specified linear transform domain. Transform learning …