adaptation of Dice log-likelihood for Stein variational gradient descent (SVGD). Specifically, we evaluate a given segmentation by constructing an "uncertainty field" over the image domain based on boundary, regional, smoothness and entropy terms. The result of this evaluation is fed into a novel algorithm that autonomously suggests regions that require user intervention. However, so far discussions have focused on 2D images only. PDF. Since our formulation of this segmentation While the acquisition functions are a straight-forward approach for classification datasets. This report provides a general introduction to active learning and a survey of the literature. For each pig, ten image pairs were acquired at end-exhale and end-inhale ventilator pauses with distinct PEEP values evenly decreasing from 20 to 2 cm H 2 O. Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentation, Interactive Radiotherapy Target Delineation with 3D-Fused Context Propagation, Active Learning Query Strategies for Classification, Regression, and Clustering: A Survey, Uncertainty-Guided Efficient Interactive Refinement of Fetal Brain Segmentation from Stacks of MRI Slices, Voxel-wise assessment of lung aeration changes on CT images using image registration: application to acute respiratory distress syndrome (ARDS), A Semi-Automated Usability Evaluation Framework for Interactive Image Segmentation Systems, An Active Learning with Two-step Query for Medical Image Segmentation, Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges, Automatic Segmentation of MRI Images for Brain Tumor using unet, Automatic Cell Counting using Active Deep Learning and Unbiased Stereology, Applications of Semisupervised and Active Learning to Interactive Contour Delineation, Spotlight: Automated Confidence-Based User Guidance for Increasing Efficiency in Interactive 3D Image Segmentation, Confidence Based Active Learning for Whole Object Image Segmentation, Intelligent scissors for image composition, Supervised hyperspectral image segmentation using active learning. (h) The final segmentation after 12 slices have been classified. To assess global recruitment, lung boundaries were first interactively delineated at inspiration, and then they were warped based on automatic image registration to define the boundaries at expiration. Confidence based active learning for whole object image segmentation. Today’s image segmentation techniques use models of deep learning for computer vision to understand, at a level unimaginable only a decade ago, exactly which real-world object is represented by each pixel of an image. It is well-known that ob-ject delineation is an ill-posed problem unless guided by the human or by apriori constraints and models. Second, realistic segmentation datasets are highly unbalanced: some categories are much more abundant than others, biasing the performance to the most represented ones. Recent advances in deep learning have achieved promising results on many biomedical image segmentation benchmarks. 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