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Showing result 1 - 5 of 35 essays matching the above criteria.
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1. Improving echocardiogram view classification using diffusion models
University essay from Göteborgs universitet/Institutionen för data- och informationsteknikAbstract : In the field of medical science datasets are often highly imbalanced, where rare datapoints are of high importance. This study aims to explore the usage of synthetic datasets to improve the classification of echocardiogram views. READ MORE
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2. Analyzing the performance of active learning strategies on machine learning problems
University essay from Uppsala universitet/Avdelningen för systemteknikAbstract : Digitalisation within industries is rapidly advancing and data possibilities are growing daily. Machine learning models need a large amount of data that are well-annotated for good performance. To get well-annotated data, an expert is needed, which is expensive, and the annotation itself could be very time-consuming. READ MORE
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3. Analyzing the Influence of Synthetic andAugmented Data on Segmentation Model
University essay from Luleå tekniska universitet/Institutionen för system- och rymdteknikAbstract : The field of Artificial Intelligence (AI) has experienced unprecedented growth in recent years, thanks to the numerous applications related to speech recognition, natural language processing, and computer vision. However, one of the challenges facing AI is the requirement for large amounts of energy, time, and data to be effective and accurate. READ MORE
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4. Automatic Detection of Tumour Infiltrating Lymphocytes in Breast Cancer Whole Slide Images
University essay from Stockholms universitet/Institutionen för data- och systemvetenskapAbstract : Cancer is one of the most common diseases this century, with breast cancer being the most common form. Pathological examination is used to detect and quantify Tumour-infiltrating lymphocytes (TILs) in breast cancer Whole Slide Images (WSIs), which can be done manually or automatically. READ MORE
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5. Trainable Region of Interest Prediction: Hard Attention Framework for Hardware-Efficient Event-Based Computer Vision Neural Networks on Neuromorphic Processors
University essay from Lunds universitet/Institutionen för elektro- och informationsteknikAbstract : Neuromorphic processors are a promising new type of hardware for optimizing neural network computation using biologically-inspired principles. They can effectively leverage information sparsity such as in images from event-based cameras, and are well-adapted to processing event-based data in an energy-efficient fashion. READ MORE