Medical image analysis using deep learning has recently been prevalent, showing great performance for various downstream tasks including medical image segmentation and its sibling, volumetric image segmentation. Particularly, a typical volumetric segmentation network strongly relies on a voxel grid representation which treats volumetric data as a stack of individual voxel 'slices', which allows learning to segment a voxel grid to be as straightforward as extending existing image-based segmentation networks to the 3D domain. However, using a voxel grid representation requires a large memory footprint, expensive test-time and limiting the scalability of the solutions. In this paper, we propose Point-Unet, a novel method that incorporates the efficiency of deep learning with 3D point clouds into volumetric segmentation. Our key idea is to first predict the regions of interest in the volume by learning an attentional probability map, which is then used for sampling the volume into a sparse point cloud that is subsequently segmented using a point-based neural network. We have conducted the experiments on the medical volumetric segmentation task with both a small-scale dataset Pancreas and large-scale datasets BraTS18, BraTS19, and BraTS20 challenges. A comprehensive benchmark on different metrics has shown that our context-aware Point-Unet robustly outperforms the SOTA voxel-based networks at both accuracies, memory usage during training, and time consumption during testing. Our code is available at https://github.com/VinAIResearch/Point-Unet.
Summary
Web user interface (UI) test automation strategies have been dominated by programmable and record–playback approaches. Of these, record–playback allows creating automation tests easily and reduces the cost of test generation. However, this approach increases the cost of test maintenance due to its unstable generated locators for identifying UI objects during playback. In this paper, we propose a new approach to generating and selecting resilient and maintainable locators. Our approach consists of two parts, a new XPath construction method and selecting the best XPath to locate the target element. Our XPath construction method relies on semantic structures of Web pages to locate the target element using its neighbors. We conducted an experiment on 15 popular websites. The results show that our approach outperforms the state‐of‐the‐practice/art Selenium IDE and Robula+ in locating target elements by effectively avoiding wrong locators. It also produces more readable XPaths (hence more maintainable tests) than do these approaches.
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