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Showing 1 to 5 of 5 for “"Biomedical image analysis"”.

  1. An Attention LSTM U-Net Model for Drosophila Melanogaster Heart Tube Segmentation

    <p>Machine learning is commonly used in biomedical image analysis, as it allows automated image segmentation and identification that minimizes the need for tedious human involvement. <em>Drosophila melanogaster</em> is often used as a cardiac disease model, where optical coherence microscopy (OCM) …

    wustl Repository record for An Attention LSTM U-Net Model for Drosophila Melanogaster Heart Tube Segmentation (opens in a new tab)

  2. Converting a Neuron-Morphology Reconstruction System: Open-Science Design and Implementation

    … integrate the system into a popular toolkit for biomedical image analysis for ease-of-use and visualization; (iii) develop a test suite of both the individual components (unit testing) and across the whole system (integration tests); and (iv) ensure that the software gives reproducible results by …

    houston Repository record for Converting a Neuron-Morphology Reconstruction System: Open-Science Design and Implementation (opens in a new tab)

  3. Deep learning for automatic microscopy image analysis

    … techniques allow for the creation of detailed images of cells (or nuclei) and have been widely employed for cell studies in biological research and disease diagnosis in clinic practices.Microscopy image analysis (MIA), with tasks of cell detection, cell classification, and cell counting, etc., …

    wustl Repository record for Deep learning for automatic microscopy image analysis (opens in a new tab)

  4. Attention mechanism in deep neural networks for computer vision tasks

    … deep neural networks (DNNs) to focus on specific image features for better understanding the semantic information of the image. However, the attention mechanism is not only capable of helping DNNs understand semantics, but also useful for the feature fusion, visual cue discovering, and temporal …

    must-thes Repository record for Attention mechanism in deep neural networks for computer vision tasks (opens in a new tab)