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Showing 1 to 5 of 5 for “"biomedical image segmentation"”.

  1. 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)

  2. Learning Reconfigurable Vision Models

    … an in-context learning method for universal biomedical image segmentation. Given a query image and an example set of image-label pairs that define a new segmentation task, it produces accurate segmentation without additional training, outperforming several related methods on unseen …

    mit Repository record for Learning Reconfigurable Vision Models (opens in a new tab)

  3. Algorithm and Hardware Co-optimization for Image Segmentation in Wearable Ultrasound Devices: Continuous Bladder Monitoring

    … of wearable ultrasound devices with on-device image processing. Collaborating with Massachusetts General Hospital, we established bladder volume monitoring as the example use case. Real-time bladder monitoring can facilitate the diagnosis of post-operative urinary retention, and reduce …

    mit Repository record for Algorithm and Hardware Co-optimization for Image Segmentation in Wearable Ultrasound Devices: Continuous Bladder Monitoring (opens in a new tab)

  4. OrganixInsights: High throughput imaging and high content screening of organoids

    Advances in biomedical imaging technologies have significantly expanded the ability of researchers to study complex biological systems at cellular and subcellular resolution. In particular, three-dimensional (3D) organoid models have emerged as powerful experimental systems for investigating tissue …

    unr Repository record for OrganixInsights: High throughput imaging and high content screening of organoids (opens in a new tab)

  5. Image Restoration using Automatic Damaged Regions Detection and Machine Learning-Based Inpainting Technique

    <p>In this dissertation we propose two novel image restoration schemes. The first pertains to automatic detection of damaged regions in old photographs and digital images of cracked paintings. In cases when inpainting mask generation cannot be completely automatic, our detection algorithm …

    chapman Repository record for Image Restoration using Automatic Damaged Regions Detection and Machine Learning-Based Inpainting Technique (opens in a new tab)