Search for dissertations about: "Image classification"
Showing result 1 - 5 of 362 swedish dissertations containing the words Image classification.
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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. A path along deep learning for medical image analysis : With focus on burn wounds and brain tumors
Abstract : The number of medical images that clinicians need to review on a daily basis has increased dramatically during the last decades. Since the number of clinicians has not increased as much, it is necessary to develop tools which can help doctors to work more efficiently. READ MORE
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3. Adapting Deep Learning for Microscopy: Interaction, Application, and Validation
Abstract : Microscopy is an integral technique in biology to study the fundamental components of life visually. Digital microscopy and automation have enabled biologists to conduct faster and larger-scale experiments with a sharp increase in the data generated. READ MORE
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4. Computerized Cell and Tissue Analysis
Abstract : The latest advances in digital cameras combined with powerful computer software enable us to store high-quality microscopy images of specimen. Studying hundreds of images manually is very time consuming and has the problem of human subjectivity and inconsistency. READ MORE
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5. Automated Tissue Image Analysis Using Pattern Recognition
Abstract : Automated tissue image analysis aims to develop algorithms for a variety of histological applications. This has important implications in the diagnostic grading of cancer such as in breast and prostate tissue, as well as in the quantification of prognostic and predictive biomarkers that may help assess the risk of recurrence and the responsiveness of tumors to endocrine therapy. READ MORE