Search for dissertations about: "image Processing Machine Learning"
Showing result 1 - 5 of 85 swedish dissertations containing the words image Processing Machine Learning.
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1. Representation Learning and Information Fusion : Applications in Biomedical Image Processing
Abstract : In recent years Machine Learning and in particular Deep Learning have excelled in object recognition and classification tasks in computer vision. As these methods extract features from the data itself by learning features that are relevant for a particular task, a key aspect of this remarkable success is the amount of data on which these methods train. READ MORE
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2. Inverse problems in signal processing : Functional optimization, parameter estimation and machine learning
Abstract : Inverse problems arise in any scientific endeavor. Indeed, it is seldom the case that our senses or basic instruments, i.e., the data, provide the answer we seek. READ MORE
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3. Image Processing, Machine Learning and Visualization for Tissue Analysis
Abstract : Knowledge discovery for understanding mechanisms of disease requires the integration of multiple sources of data collected at various magnifications and by different imaging techniques. Using spatial information, we can build maps of tissue and cells in which it is possible to extract, e.g. READ MORE
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4. Robust machine learning methods
Abstract : We are surrounded by data in our daily lives. The rent of our houses, the amount of electricity units consumed, the prices of different products at a supermarket, the daily temperature, our medicine prescriptions, our internet search history are all different forms of data. Data can be used in a wide range of applications. READ MORE
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5. Advanced Machine Learning Methods for Oncological Image Analysis
Abstract : Cancer is a major public health problem, accounting for an estimated 10 million deaths worldwide in 2020 alone. Rapid advances in the field of image acquisition and hardware development over the past three decades have resulted in the development of modern medical imaging modalities that can capture high-resolution anatomical, physiological, functional, and metabolic quantitative information from cancerous organs. READ MORE