Active learning has been recently introduced to the field of image segmentation. We propose an active learning approach to image segmentation that exploits geometric priors to speed up and streamline the annotation process. Share on. Finally, we will present various experimental results and in particular some examples for which the classical snakes methods based on the gradient are not applicable. This report provides a general introduction to active learning and a survey of the literature. 2019 Sep 3;335:34-45. doi: 10.1016/j.neucom.2019.01.103. However, annotating biomedical images is knowledge-driven, time-consuming, and labor-intensive, makin … Epub 2019 Feb 7. We present a new active learning strategy for semantic segmentation based on deep reinforcement learning (RL). Strategies are needed to explore architecture design spaces more efficiently, reducing the number of evaluations required to obtain good solutions. In recent years, usage of deep learning is rapidly proliferating in almost every domain, especially in medical image processing, medical image analysis, and bioinformatics. However, so far discussions have focused on 2D images only. Active learning (AL), learns from a few labeled data instances with the additional facility of querying the labels of instances from an expert annotator or oracle. Learning Loss for Active Learning. Would you like email updates of new search results? The approach has potential applications in medical image segmentation and content-based Sourati J, Gholipour A, Dy JG, Tomas-Fernandez X, Kurugol S, Warfield SK. ∙ LUNDS TEKNISKA HÖGSKOLA ∙ 0 ∙ share . Clickers engage students during class and provide real-time feedback that can allow both students and. We propose an active learning (AL) framework to select most informative samples and add to the training data. 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. Amrehn M, Steidl S, Kortekaas R, Strumia M, Weingarten M, Kowarschik M, Maier A. Int J Biomed Imaging. While clickers had an overall positive effect on student exam performance, we found that this effect was significantly greater in higher-performing students, with lower-performing students showing little-to-no benefit. Epub 2019 Mar 27. This includes a discussion of the scenarios in which queries can be formulated, and an overview of the query strategy frameworks proposed in the literature to date. 2020 Feb 22;10(2):118. doi: 10.3390/brainsci10020118. MediaTable allows users to, Active learning strategies have been increasingly adopted in higher education across many science, technology, engineering, and math (STEM) disciplines. PDF. The predictive model used here is an ensemble method, known as random forest. Epub 2017 Apr 23. We validate our method against random plane selection showing an average DSC improvement of 10% in the first five plane suggestions (batch queries). Video for ICCV 2015 for paper 'Introducing Geometry in Active Learning for Image Segmentation' by Ksenia Konyushkova, Raphael Sznitman and Pascal Fua During the interaction, our proposed approach reuses the extant extracted features and does not alter the existing 3D CNN model architectures, avoiding the perturbation on other predictions. Various uncertainty estimation methods and acquisition strategies (regions and full images) are investigated. 2012 Apr;39(4):2214-28. doi: 10.1118/1.3696376. Results indicate a significant increase in the number of relevant items found for the two groups of users using bucket expansions, yielding the best results with fully automatic bucket expansions, thereby aiding high recall video retrieval significantly. Home Browse by Title Proceedings MRCS'06 Confidence based active learning for whole object image segmentation. First, acquiring pixel-wise labels is expensive and time-consuming. Join ResearchGate to find the people and research you need to help your work. The technique is based on active contours evolving in time according to intrinsic geometric measures of the image. Active Learning for Biomedical Image Segmentation Vishwesh Nath, Dong Yang, Bennett A. Landman, Daguang Xu, Holger R. Roth NVIDIA, Bethesda, USA Contact: email@example.com, firstname.lastname@example.org Abstract Active learning is a unique abstraction of machine learning techniques where the model/algorithm could guide users for annotation of a set of data points … 2007. Our model can detect objects whose boundaries are not necessarily defined by gradient. Active Learning for Semantic Segmentation with Expected Change Alexander Vezhnevets 1Joachim M. Buhmann Vittorio Ferrari2 1ETH Zurich 2The University of Edinburgh Zurich, Switzerland Edinburgh, UK Abstract We address the problem of semantic segmentation: clas-sifying each pixel in an image according to the semantic Also, more advanced query strategies based on reinforcement learning and deep learning, along with query strategies under the realistic environment setting, are presented. 2019 Nov;38(11):2642-2653. doi: 10.1109/TMI.2019.2907805. While current active learning in biomedical segmentation has been focused towards different acquisition schemes for annotation of data by the oracle (expert human annotator), other domains have also investigated reinforcement learning and proactive learning for active learning . Many instructors that implement clickers also implement peer instruction, where students vote individually, discuss the question with their peers, and then revote. HHS This process results in a refined training dataset, which helps in minimizing the overall cost. However, so far discussions have focused on 2D images only. object. It can be applied for both background–foreground and multi-class segmentation tasks in 2D images and 3D image … A circuit synthesis problem is used to test the active learning strategy; two complete data sets for this case study are available, aiding analysis. Authors: Aiyesha Ma. We observed that both clicker question formats had similar effects on later exam performance. Active learning has been recently introduced to the field of image segmentation. The approach has met with increasingly positive reviews due to testing the first two components on second-year medical student volunteers before its implementation, keeping the first component as simple as possible, keeping the second component to <30 min, and continued revision of the third component to increase clinical context of the study questions. However, so far discussions have focused on 2D images only. For instance, Dutt Jain & Grauman (2016) combine metrics (deﬁned on hand-crafted heuristics) that encourage the diversity and representativeness of labeled samples. When the gestured mouse position comes in proximity to an object edge, a live-wire boundary "snaps" to, and wraps around the object of interest. Abstract: Augmenting spectral data with spatial information for image classification has recently gained significant attention, as classification accuracy can often be improved by extracting spatial information from neighboring pixels. 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. We propose a novel method for applying active learning … NIH An active learner may ask queries in the form of unlabeled instances to be labeled by an oracle (e.g., a human annotator). In this paper we present the selective image segmentation problem as a classification problem, and use active Embodied Visual Active Learning for Semantic Segmentation. This geodesic approach for object segmentation allows to connect classical "snakes" based on energy minimization and geometric active contours based on the theory of curve evolution. Experimental results show that: (1) our proposed CNN obtains uncertainty estimation in real time which correlates well with mis-segmentations, (2) the proposed interactive level set is effective and efficient for refinement, (3) UGIR obtains accurate refinement results with around 30% improvement of efficiency by using uncertainty to guide user interactions. Neuroimage. 2019 Sep 5;2019:1464592. doi: 10.1155/2019/1464592. Local-recruitment maps overlaid onto the original images were visually consistent, and the sum of these values over the whole lungs was very close to the global-recruitment estimate, except four outliers. We minimize an energy which can be seen as a particular case of the minimal partition problem. This paper. From this standpoint, the system uses a classi-ﬁer with some form of prior knowledge of objects and their appearance in images. ML4H: Machine Learning for Health Workshop at NIPS 2017, Long Beach, CA, USA, In Press. Several query strategies are compared. Audience response systems, or clickers, are useful tools that allow instructors to incorporate active learning into large-enrollment courses. Image segmentation is one of the most essential biomedical image processing problems for different imaging modalities, including microscopy and X-ray in the Internet-of-Medical-Things (IoMT) domain. From a machine learning perspective, interactive image segmentation can be viewed as a few-shot active learning problem. The prediction of all questionnaire results has an average relative error of 8.9%, which is close to the expected precision of the questionnaire results themselves. Furthermore, our user study shows that our method saves the user 64% of their time, on average. Annotation/Labeling is an expensive activity especially in biomedical area. Our Active Bucket Categorization approach augments this by unobtrusively expanding these buckets with related footage from the whole collection. We designed a study in which students in an introductory biology course engaged in clickers with peer discussion during class. While the acquisition functions are a straight-forward approach for classification datasets. 1. The second component was a small-group problem-solving session that each group conducted immediately after their patient simulator session. The positive effect of in-class clicker questions on later exams depends on initial student performa... Conference: Lecture Notes in Computer Science, Medical Image Computing and Computer-Assisted Intervention (MICCAI). A Semi-Automated Usability Evaluation Framework for Interactive Image Segmentation Systems. Deep Learning (DL) algorithms enabled computational models consist of multiple processing layers that represent data with multiple levels of abstraction. We propose an active learning approach to image segmentation that exploits geometric priors to speed up and streamline the annotation process. 2: Results of active learning based on mean Entropy and variance of … Fully auto- mated segmentation is an unsolved problem, while manual tracing is inaccurate and laboriously unacceptable. An agent learns a policy to select a subset of small informative image regions -- opposed to entire images -- to be labeled, from a pool of unlabeled data. Generally, data is available abundantly in unlabeled form, and its annotation requires some cost. The result of this evaluation is fed into a novel algorithm that autonomously suggests regions that require user intervention. Finally, implementation guide, applications, and challenges of AL are discussed. Here, we frame interactive 3D image segmentation as a classification problem and incorporate active learning in order to alleviate the user from choosing where to provide interactive input. An agent learns a policy to select a subset of small informative image regions -- opposed to entire images -- to be labeled, from a pool of unlabeled data. While this strategy has been shown to improve conceptual understanding, the effects of specific factors, such as question format and student performance level, on learning gains remains unclear. One baseline group uses only the categorization features of MediaTable such as sorting and filtering on concepts and fast grid preview, but no online learning mechanisms. active learning and segmentation propagation. A short summary of this paper. Also, the initial curve can be anywhere in the image, and interior contours are automatically detected. 2007 Aug;24(4):742-7. Active learning for semantic segmentation has been relatively less explored than other tasks, potentially due to its large-scale nature. Yang, L., Zhang, Y., et al. Results Boundary cooling automatically freezes unchanging seg- ments and automates input of additional seed points. Cooling also allows the user to be much more free with the gesture path, thereby increasing the efficiency and finesse with which boundaries can be extracted. Local-recruitment map was calculated as follows: For each voxel at expiration, the matching location at inspiration was determined by image registration, non-aerated voxels were counted in the neighborhood of the respective locations, and the voxel count difference was normalized by the neighborhood size. Active learning has been applied to many disciplines like object detection (Sivaraman & Trivedi, 2014), semantic segmentation (Vezhnevets et al., 2012), image classification … Robustness is further enhanced with on-the-fly training which causes the boundary to adhere to the specific type of edge currently being followed, rather than simply the strongest edge in the neigh- borhood. The dispersion of global- and regional-recruitment values decreased when using image registration, compared to the conventional approach neglecting tissue motion. Students subsequently answered an isomorphic exam question 1–3 weeks later. (g) The 3 rd AL query slice. A comparison of the proposed method with state-of-the-art competitors shows its effectiveness. Nevertheless, publications introducing novel interactive segmentation systems (ISS) often lack an objective comparison of HCI aspects. Active learning has been recently introduced to the field of image segmentation. (c) A slice through the uncertainty field. To provide experts an efficient way to modify the CNN predictions without retrain the model, we propose 3D-fused context propagation, which propagates any edited slice to the whole 3D volume. Active learning is well-motivated in many modern machine learning problems, where unlabeled data may be abundant but labels are difficult, time-consuming, or expensive to obtain. Methods adaptation of Dice log-likelihood for Stein variational gradient descent (SVGD). problem uses human interaction, active learning is used for training to minimize the training effort needed to segment the The second row shows the segmentation of the iliac bones in a pelvis CT image. We will discuss how this problem can be naturally translated to a semi-supervised and active learning problem and we will de-scribe our work so far towards investigating the issues involved. We present a new active learning strategy for semantic segmentation based on deep reinforcement learning (RL). Second, realistic segmentation datasets are highly unbalanced: some categories are much more abundant than others, biasing the performance to the most represented ones. The labeling, as well as learning cost, can be minimized by learning with the minimum labeled data instances. Download PDF Package. Learning-based approaches for semantic segmentation have two inherent challenges. An integrated active learning approach can enhance student interest in integrating cardiovascular-renal physiology, particularly if faculty members are willing to revise the approach in response to student feedback.
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