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 20 of 48 for “"Medical image analysis"”.
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Statistical models in medical image analysis
Computational tools for medical image analysis help clinicians diagnose, treat, monitor changes, and plan and execute procedures more safely and effectively. Two fundamental problems in analyzing medical imagery are registration, which brings two or more datasets into correspondence, and …
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Multimodal Representation Learning for Medical Image Analysis
… that exploit multimodal clinical data to improve medical image analysis. Medical images capture rich information of a patient’s physiological and disease status, central in clinical practice and research. Computational models, such as artificial neural networks, enable automatic and quantitative …
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Data fusion techniques for nondestructive evaluation and medical image analysis
… surveillance and autonomous vehicle control), medical diagnosis and structural health monitoring. Techniques for data fusion have been drawn from areas such as statistics, image processing, pattern recognition and computational intelligence. This dissertation includes investigation and …
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VoxelPrompt: A Vision-Language Agent for Grounded Medical Image Analysis
… through joint modeling of natural language, image volumes, and analytical metrics. VoxelPrompt is multi-modal and versatile, leveraging the flexibility of language interaction while providing quantitatively-grounded image analysis. Given a variable number of 3D medical volumes, such as MRI …
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Domain and User-Centered Machine Learning for Medical Image Analysis
… tasks, such as analyzing and interpreting medical images, to lighten the burden on radiologists and avoid a further increase in healthcare expenditure. Machine learning (ML), including deep learning (DL) offer a potential solution as these algorithms can learn to automatically recognize …
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Medical Image Analysis Based on Graph Machine Learning and Variational Methods
… complexity and diversity of tumor sub-regions in medical imaging. We introduce a novel approach utilizing Graph Neural Networks (GNNs) that incorporate both spectral and spatial insights for segmentation. By leveraging various supervoxel creation methods such as VCCS, SLIC, Watershed, Meanshift, …
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Automatic Pancreas Segmentation and 3D Reconstruction for Morphological Feature Extraction in Medical Image Analysis
… of highly accurate, quantitative automatic medical image segmentation techniques, in comparison to manual techniques, remains a constant challenge for medical image analysis. In particular, segmenting the pancreas from an abdominal scan presents additional difficulties: this particular organ …
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MEDICAL IMAGE BASED PREOPERATIVE ANALYSIS AND INTRAOPERATIVE NAVIGATION
In recent years, medical image processing, artificial intelligence, computer-assisted system and robotic intervention are gradually applied to medicine to help surgeons for medical image analysis and surgical navigation, promoting the vigorous progress of precision medicine. This thesis mainly …
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Scale-Driven Image Decomposition With Applications to Recognition, Registration, and Segmentation
… computer vision, public security, surveillance, medical image analysis, and other related fields.
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Learning Deformable Templates for Brain MRI
Deformable templates, or atlases, are images, often labelled, that represent a typical anatomy for a population. They are commonly used in medical image analysis for population studies and computational anatomy tasks. Practitioners use image alignment techniques to compare the subject scan and the …
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Developing Deep-Learning Methods for Diagnosis and Prognosis of Pediatric Progressive Diseases Using Modern Imaging Techniques
… a standard part of patient care. However, such analysis of neuroimaging is time- and labor-intensive. Automated approaches to these tasks are needed to improve speed, accuracy, and availability. Automated medical image analysis tools based on 3D/2D deep learning algorithms can help improve the …
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Geometric Deep Learning for Healthcare Applications
… a subset of Geometric Deep Learning methods, for medical image analysis and causal structure learning. Tracking the progression of pathologies in chest radiography poses several challenges in anatomical motion estimation and image registration as this task requires spatially aligning the …
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Visualization and analysis of large medical image collections using pipelines
Medical image analysis often requires developing elaborate algorithms that are implemented as computational pipelines. A growing number of large medical imaging studies necessitate development of robust and flexible pipelines. In this thesis, we present contributions of two kinds: (1) an open …
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Comparative analysis of deep learning and graph cut algorithms for cell image segmentation
Image segmentation is a commonly used technique in digital image processing with many applications in the area of computer vision and medical image analysis. The goal of image segmentation is to partition an image into multiple regions, normally based on the characteristics of pixels in a given …
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Deep learning and localized features fusion for medical image classification
<p>"Local image features play an important role in many classification tasks as translation and rotation do not severely deteriorate the classification process. They have been commonly used for medical image analysis. In medical applications, it is important to get accurate diagnosis/aid results in …
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A unified information theoretic framework for pair- and group-wise registration of medical images
The field of medical image analysis has been rapidly growing for the past two decades. Besides a significant growth in computational power, scanner performance, and storage facilities, this acceleration is partially due to an unprecedented increase in the amount of data sets accessible for …
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Augmented Radiology in High Grade Serous Ovarian Carcinoma
… imaging is at the centre of how modern medical care is delivered and provides a unique perspective on disease: it is non-invasively attained, gives 3-dimensional spatially resolved information about disease, and when images are captured sequentially, allows for temporal comparison. …
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Computational methods for medical image registration
… to the improvement of techniques in the field of medical image analysis. Imaging modalities have been improved and new acquisition methods have been introduced to reveal greater anatomical detail and to allow for more information to be extracted from medical images. However, certain challenges …
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Deep learning approach to discontinuity-preserving image registration
Image registration is an indispensable tool in medical image analysis. Traditionally, registration algorithms are aimed at aligning image pairs using regularizers to impose smoothness restrictions on unknown deformation fields. The majority of these methods assume global smoothness in the image …
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Detecting cells and analyzing their behaviors in microscopy images using deep neural networks
<p>"The computer-aided analysis in the medical imaging field has attracted a lot of attention for the past decade. The goal of computer-vision based medical image analysis is to provide automated tools to relieve the burden of human experts such as radiologists and physicians. More specifically, …
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