To the best of our knowledge, all current approaches for active learning in semantic segmentation rely on hand-crafted active learning heuristics. Figure 0(a) shows how to build the state representation and Figure 0(b) how to compute the action representation of a particular region. However, learning a labelling policy from the data could allow the query agent to ask for labeled data as a function of the data characteristics and class imbalances, that may vary between datasets. 1 (up), a deep image segmentation model N is divided into a heavy feature extraction part Nfeat and a light task-related part Ntask. Nianyin Zeng was born in Fujian Province, China, in 1986. For instance, Dutt Jain and Grauman (2016) combine metrics (defined on hand-crafted heuristics) that encourage the diversity and representativeness of labeled samples. He is currently working toward the master’s degree in electrical testing technology and instruments at Xiamen University, Xiamen, China. In this case, the action at is composed of K independent sub-actions {akt}Kk=1, each with a restricted action space, avoiding the combinatorial explosion of the action space. This is highly inefficient, since each step involves updating the segmentation network and computing the rewards. A novel image segmentation method is developed in this paper for quantitative analysis of GICS based on the deep reinforcement learning (DRL), which can accurately distinguish the test line and the control line in the GICS images. Settles et al. competitive baseline to reach the same performance. We compare the validation IoU when asking for pixel-wise labels for entire images versus pixel-wise labels for small regions. share. The first is FirstP-Net, whose goal is to find the first edge point and generate a probability map of the edge points positions. 02/16/2020 ∙ by Arantxa Casanova, et al. International Workshop on Automatic Selection, Configuration and Composition of Machine Learning Algorithms, The cityscapes dataset for semantic urban scene understanding, Committee-based sampling for training probabilistic classifiers, Dropout as a bayesian approximation: representing model uncertainty in deep learning, Learning how to actively learn: a deep imitation learning approach, Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. ∙ Appendix. We use the same learning rate for both the segmentation and query networks; We show in Figure C.1 a more detailed plot for the class frequencies of regions that each of the methods chooses for labeling. (ii) H is an uncertainty sampling method that selects the regions with maximum cumulative pixel-wise Shannon entropy, Here, we analyze the incremental effect of our design choices for the state and action representation on Cityscapes. 2 Department of Electrical and Computer Engineering, University of Waterloo, Watrloo, Canada. Pool sizes were selected according to the best validation mean IoU. In … We are interested in finding a policy to select samples that maximize the segmentation performance. As it is shown in Table E.2, asking for entire image labels has similar performance for all methods, that resemble Uniform performance when asking for region labels. Others focus on foreground-background segmentation of biomedical images (Gorriz et al., 2017; Yang et al., 2017), also using hand-crafted heuristics. The state set is chosen to be representative of DT, by restricting the sampling of DS to have a similar class distribution to the one of DT. Matthew Lai is a research engineer at Deep Mind, and he is also the creator of “Giraffe, Using Deep Reinforcement Learning to Play Chess”. Results of varying the number of regions to be labeled at each step by our method. doi: 10.1109/JBHI.2020.3008759. For each candidate region, x in a pool Pkt, we compute the KL divergence between the class distributions of the prediction map of region x (estimated as normalized counts of predicted pixels in each category) and the class distributions of each labeled and unlabeled regions (using the ground-truth annotations and network predictions, respectively). degrees in electronic and communication from King AbdulAziz University, Jeddah, Saudi Arabia, in 1996 and 2002. Recently, reinforcement learning has gained attention as a method to learn a labelling policy that directly maximizes the learning algorithm performance. ∙ communities, © 2019 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. This work was supported in part by International Science and Technology Cooperation Project of Fujian Province of China under Grant 2019I0003, in part by the Korea Foundation for Advanced Studies, in part by the Fundamental Research Funds for the Central Universities of China under Grant 20720190009, in part by The Open Fund of Engineering Research Center of Big Data Application in Private Health Medicine of China under Grant KF2020002, and in part by The Open Fund of Provincial Key Laboratory of Eco-Industrial Green Technology-Wuyi University of China. Contrary to them, we deal with a much more complex problem: semantic segmentation versus simple classification on UCI repository (Dua and Graff, 2017). In this work, we propose a reinforcement learning-based approach to search the best training strategy of deep neural networks for a specific 3D medical image segmentation task. (2018) focus on cost-effective approaches, proposing manually-designed acquisition functions based on the cost of labeling images or regions of images. Surprisingly, B is worse than U, , specially for small budgets, where training with the newly acquired labels does not provide any additional information. Search. Moreover, we need to adapt the DQN formulation to allow the problem to be computationally feasible. This is the code for "Medical Image Segmentation with Deep Reinforcement Learning" The proposed model consists of two neural networks. The weights are updated at each step of the active learning loop, by sampling batches of 16 experience tuples from an experience replay buffer, sized 600 and. The goal is to alleviate the costly process of obtaining pixel-wise labels with a human in the loop. method proposes a new modification of the deep Q-network (DQN) formulation for The region selection decision is made based on In this paper, we are interested in focusing human labelling Specially, it asks labels for more Person, Rider, Train, Motorcycle and Bicycle pixels. Segmentation, Importance of Self-Consistency in Active Learning for Semantic Mackowiak et al. His research interests include big data analysis and deep learning techniques. Advertisement. Each episode e elapses a total of T steps. convolu... We compare our method qualitatively with the baselines in Figure D.1. We chose K= 256 regions per step. He is the author or co-author of several technical papers and also a very active reviewer for many international journals and conferences. Segmentation, Embodied Visual Active Learning for Semantic Segmentation, DEAL: Difficulty-aware Active Learning for Semantic Segmentation, MetaBox+: A new Region Based Active Learning Method for Semantic Get the latest machine learning methods with code. This can lead to undesired biases and performance properties for learned models. Image segmentation technology has made a remarkable effect in medical image analysis and processing, which is used to help physicians get a more accurate d . The action selection and evaluation is decoupled; the action is selected with the target network and is evaluated with the query network. In the third image, it asks for labels of the traffic lights and a pedestrian on a bicycle. Long, E. Shelhamer, and T. Darrell (2015), Fully convolutional networks for semantic segmentation, R. Mackowiak, P. Lenz, O. Ghori, F. Diego, O. Lange, and C. Rother (2018), Cereals-cost-effective region-based active learning for semantic segmentation, V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller (2013), Playing atari with deep reinforcement learning, M. Müller, A. Dosovitskiy, B. Ghanem, and V. Koltun (2018), Driving policy transfer via modularity and abstraction, Balancing exploration and exploitation: a new algorithm for active machine learning, A. Padmakumar, P. Stone, and R. Mooney (2018), Learning a policy for opportunistic active learning, K. Pang, M. Dong, Y. Wu, and T. Hospedales (2018), Meta-learning transferable active learning policies by deep reinforcement learning, Recurrent convolutional neural networks for scene labeling, Meta-learning for batch mode active learning, S. R. Richter, V. Vineet, S. Roth, and V. Koltun (2016), Playing for data: Ground truth from computer games, O. Ronneberger, P. Fischer, and T. Brox (2015), U-net: convolutional networks for biomedical image segmentation, Toward optimal active learning through monte carlo estimation of error reduction, M. Schwarz, A. Milan, A. S. Periyasamy, and S. Behnke (2018), RGB-d object detection and semantic segmentation for autonomous manipulation in clutter, Active learning for convolutional neural networks: a core-set approach, B. Nowadays, semantic segmentation is one of the key problems in the field of computer vision. First, acquiring pixel-wise labels is expensive and time-consuming. The query network π is trained on DT with a small, fixed budget (0.5k regions for Camvid and 4k regions for Cityscapes) to encourage picking regions that will boost the performance in an heavily scarce data regime. Note that the baselines do not have any learnable component. We aim at learning a policy from the data that finds the most informative regions on a set of unlabeled images and asks for its labels, such that a segmentation network can achieve high-quality performance with a minimum number of labeled pixels. Between 1996 and 2005, he worked in Jeddah as a communication instructor in the College of Electronics & Communication. To stabilize the training, we used a target network with weights ϕ′ and the double DQN (Van Hasselt et al., 2016) formulation. We present a new active learning strategy for semantic segmentation based on deep reinforcement learning (RL). The region selection decision is made based on predictions and uncertainties of the segmentation model being trained. 08/13/2020 ∙ by Sharat Agarwal, et al. Figure 0(b) illustrates how we represent each possible action in a pool. share, We present a novel region based active learning method for semantic imag... it used to locate boundaries & objects. Our method proposes a new modification of the deep Q-network (DQN) formulation for active learning, … Moreover, by directly optimizing for the per-class mean IoU and defining class-aware representations for states and actions, our method asks for more labels of under-represented classes compared to baselines. ScienceDirect ® is a registered trademark of Elsevier B.V. ScienceDirect ® is a registered trademark of Elsevier B.V. Deep-reinforcement-learning-based images segmentation for quantitative analysis of gold immunochromatographic strip, Fundamental Research Funds for the Central Universities of China. We can tackle the aforementioned problems by selecting, in an efficient and effective way, which regions of the images should be labeled next. By continuing you agree to the use of cookies. Out of these, 10 images represent DS, 150 build DT and 200, DR, where we get our rewards. We rely on a DQN (Mnih et al., 2013), parameterized by ϕ, to find an optimal policy. As seen in Table E.1, using only the max-pooled entropy map (Ours - 1H), the performance is slightly worse than H. When we combine the information of the 3 pooled entropy maps (Ours - 3H), we outperform H baseline. Title: Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation. (2018); Bachman et al. Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday. The query agent selects K sub-actions {akt}Kk=1 with ϵ-greedy policy. Selecting regions, instead of entire images, allows the algorithm to focus on the most relevant parts of the images, as shown in Figure 1. Because our method maximizes the mean IoU per class, it indirectly learns to ask for more labels of regions with under-represented classes, compared to the baselines. The second set of features (ii) is thus obtained by flattening these entropy features and concatenating them. In this context, segmentation is formulated as learning an image-driven policy for shape evolution that converges to the object boundary. A natural question that arises is how to develop learning … Reinforcement learning is one of modern machine learning technologies in which learning is carried out through interaction with the environment. degree in computing from Hohai University, Nanjing, China, in 1982 and the Ph.D. degree in computer science from Heriot-Watt University, Edinburgh, U.K., in 1988. Professor Liu serves on editorial boards of four computing journals, founded the biennial international conference series on IDA in 1995, and has given numerous invited talks in bioinformatics, data mining and statistics conferences. We use 20 iterations of MC-Dropout (Gal and Ghahramani, 2016) (instead of 100, as in (Gal et al., 2017)) for computational reasons. We thank NSERC and PROMPT. 0 The agent uses these objective reward/punishment to … Meanwhile, the multi-factor learning curve is introduced in the DRL … Contribution to the validation mean IoU performance [%] of Cityscapes dataset, for a budget of 4K and for each of the components of our state representation, compared to the baselines. Notice that lung segmentation exhibits a bigger gain due to the task relevance. The state path and action path are composed of 4 and 3 layers, respectively, with a final layer that fuses them together to get the global features; these are gated with a sigmoid, controlled by the KL distance distributions in the action representation. The MDP is defined with the sequence of transitions {(st,at,rt+1,st+1)}. These works have examined both stream-based active learning (Fang et al., 2017; Woodward and Finn, 2016), where unlabeled samples are provided one by one, and the decision is to label it or not, and pool-based active learning (Konyushkova et al., 2018), where all the unlabeled data is provided beforehand, and the decision is later taken on which samples to choose. We propose a new modification of DQN formulation to learn the acquisition function, adapted to the large-scale nature of semantic segmentation. Image segmentation is a well-suited domain for advances in few-shot learning given that the labels are particularly costly to generate. Settles, M. Craven, and L. Friedland (2008), Active learning with real annotation costs, C. H. Sudre, W. Li, T. Vercauteren, S. Ourselin, and M. J. Cardoso (2017), Deep learning in medical image analysis and multimodal learning for clinical decision support, Learning to predict by the methods of temporal differences, H. Van Hasselt, A. Guez, and D. Silver (2016), Deep reinforcement learning with double q-learning, A. Vezhnevets, J. M. Buhmann, and V. Ferrari (2012), Active learning for semantic segmentation with expected change, S. Vijayanarasimhan and K. Grauman (2009), What’s it going to cost you? Each sub-action ak,nt is a concatenation of four different features: the entropy and class distribution features (as in the state representation), a measure of similarity between the region xk and the labeled set and another between the region and the unlabeled set. To the best of our knowledge, our work is the first to apply data-driven RL-based approach to the problem of active learning for semantic segmentation. Performance of several methods with increasing active learning budget, expressed as the number of 128, Per category IoU and mean IoU [%] on Cityscapes validation set, for a budget of 12k regions. Join one of the world's largest A.I. ∙ Similar to our work, they use a region-based approach to cope with the large number of samples on a segmentation dataset. He received the B.Sc. Figure 3, shows the entropy of the distribution of selected pixels of the final labeled set (for a budget of 12k regions) for Cityscapes. Especially, Dr. Zeng is an ISEF Fellow founded by the Korea Foundation for Advance Studies and also a Visiting Professor at the Korea Advanced Institute of Science and Technology from September 2017. degree in mathematics in 1986 from Suzhou University, Suzhou, China, and the M.Sc. He published widely in the top IEEE communications conferences and journals and has received the Carter award, University of Leeds for the best PhD. In general, all results have a high variance due to the low regime of data we are working in. The query network is evaluated on a different split DV. Note that states and actions do not depend on the specific architecture of the segmentation network. However, the bounded core-set loss used tends to perform worse when the number of classes grows. We start this section by describing the datasets that we use to evaluate our method, the experimental setup, and the baselines. Each of the layers are composed of Batch Normalization, ReLU activation and a fully-connected layer. In our setting, taking an action means asking for the pixel-wise annotation of an unlabeled region. For the unlabeled set we follow the same procedure, resulting in another distribution of KL divergences. He, M. Ostendorf, X. He has published around 600 papers in refereed international journals. In this section, we provide illustrations that show more details on how the state and action are built. From October 2012 to March 2013, he was a Research Assistant in the Department of Electrical and Electronic Engineering at the University of Hong Kong. reinforcement learning(RL). Our method works specially well for under-represented classes, such as Person, Motorcycle or Bicycle, among others. Each image is split in 128 regions of dimension 128×128. Figure 0(a) illustrates how st is computed from each region. This improves the performance and helps to mitigate class imbalance. Second, realistic segmentation datasets are highly unbalanced: some categories (2018); Padmakumar et al. Baselines B and H select some of those relevant regions, but miss a lot of them. State of the art methods for semantic image segmentation are trained in ... . For example, annotation and quality control required more than 1.5h per image (on average) on Cityscapes (Cordts et al., 2016), a popular dataset used for benchmarking semantic segmentation methods. Entropy of class distributions obtained from pixels of selected regions. 333We also tried to optimize the network with an average of Q-values over all sub-actions as in y=rt+1+1K∑kQ(st+1,akt+1) and Q(st,at)=1K∑kQ(st,akt), but it performed worse. We evaluate the final segmentation performance (measured in mean IoU) on the test set of CamVid and on the validation set of Cityscapes. The desired query agent should follow an optimal policy. We introduce a new method for the segmentation of the prostate in transrectal ultrasound images, using a reinforcement learning scheme. It is also composed of real street scene views, with image resolution of 2048×1024 and 19 semantic categories. In the second row,“24 R” results for labeling 24 regions at each step. And since, this is not a traditional conference … Generally, such systems are open loop with no feedback between levels and assuring their robustness is a key challenge in computer vision … Reinforcement Learning for Visual Object Detection ... ground segmentation with Gestalt, ‘object-like’ filtering[5], superpixels[38, 32] or edge-based cues[21]. We present a new active learning strategy for semantic segmentation based on deep reinforcement learning (RL). Moreover, we measure the uncertainty of the predictor with the entropy over the probability of predicted classes. Gold immunochromatographic strip (GICS) is a widely used lateral flow immunoassay technique. The SCRL, for the first time, applies deep reinforcement learning into VB detection and segmentation. As data augmentation, we use random horizontal flips and random crops of. For each region, we compute the entropy of each pixel location to obtain a spatial entropy map. represented ones. In this paper, we propose a Sequential Conditional Reinforcement Learning network (SCRL) to tackle the simultaneous detection and segmentation of VBs from MR spine images. The first set of features (i) is a (normalized) count of the number of pixels that are predicted to each category. We cast the AL problem within a Markov decision process (MDP) formulation, inspired by other work such as. Secondly, medical image segmentation methods However, to obtain more informative features, we compute a normalized histogram of KL divergence scores, resulting in a distribution of similarities. We show that our proposed method can help mitigate the problem at its source, i.e. Nevertheless, to fully exploit the potentials of neural networks, we propose an automated searching approach for the optimal training strategy with reinforcement learning. In this split is where all methods perform active learning, in the setting where we mask out the labels (. large-scale dataset Cityscapes. ∙ This policy maps each state to an action that maximizes the expected sum of future rewards. : predicting effort vs. informativeness for multi-label image annotations, K. Wang, D. Zhang, Y. Li, R. Zhang, and L. Lin (2017), Cost-effective active learning for deep image classification, IEEE Transactions on Circuits and Systems for Video Technology, DSNet for real-time driving scene semantic segmentation, L. Yang, Y. Zhang, J. Chen, S. Zhang, and D. Z. Chen (2017), Suggestive annotation: a deep active learning framework for biomedical image segmentation, L. Yu, X. Chen, G. Gkioxari, M. Bansal, T. L. Berg, and D. Batra (2019), Class frequencies [%] in Cityscapes for the selected regions to label after the active learning acquisition for different methods. Copyright © 2021 Elsevier B.V. or its licensors or contributors. He, J. Chen, J. Gao, L. Li, and L. Deng (2016), Deep reinforcement learning with a combinatorial action space for predicting popular reddit threads, N. Houlsby, F. Huszar, Z. Ghahramani, and M. Lengyel (2011a), Bayesian active learning for classification and preference learning, N. Houlsby, F. Huszár, Z. Ghahramani, and M. Lengyel (2011b), A. Kirillov, R. B. Girshick, K. He, and P. Dollár (2019), K. Konyushkova, R. Sznitman, and P. Fua (2015), Introducing geometry in active learning for image segmentation, K. Konyushkova, R. Sznitman, and P. Fua (2017), K. Konyushkova, R. Sznitman, and P. Fua (2018), Discovering general-purpose active learning strategies, Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner (1998), Gradient-based learning applied to document recognition, T. Lin, P. Dollár, R. B. Girshick, K. He, B. Hariharan, and S. J. Belongie (2017), Feature pyramid networks for object detection, M. Liu, W. Buntine, and G. Haffari (2018), Multi-class multi-annotator active learning with robust gaussian process for visual recognition, J. We consider the termination of each episode when the budget B of labeled regions is met, i.e., |Lt|=B. Recently, the 3rd category emerges: Reinforcement Learning (action-based learning based on certain defined rewards). Contrary to us, their labelling strategy is based on manually defined heuristics, limiting the representability of the acquisition function. The region selection decision is made based on predictions and uncertainties of the segmentation … We start by setting the segmentation network f to a set of initial weights θ0 and with no annotated data, i.e., L0=∅ and U0=DT. ∙ 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. 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. An agent learns a policy to select a subset of Once the episode is terminated, we restart the weights of the segmentation network f to the initial weights θ0, set L0=∅ and U0=DT, and restart the episode. The performance is measured with a standard semantic segmentation metric, Intersection-over-Union (IoU). The proposed approach can be utilized for tuning hyper-parameters, and selecting necessary data augmentation with certain probabilities. Figure 3(a) shows results on CamVid for different budget sizes. Here, we also observe that our method outperforms the baselines for all budgets points. The agent is provided with a scalar reinforcement signal determined objectively. We split the train set with uniform sampling in 110 labeled images (from where we get 10 images to represent the state set DS and the rest for DT), and 260 images to build DV, where we evaluate and compare our acquisition function to the baselines. Currently, he is an Assistant Professor with the Department of Instrumental & Electrical Engineering of Xiamen University. Table 1 shows the per-class IoU for the evaluated methods (with a fixed budget). The action at={akt}Kk=1, composed of K sub-actions, is a function of the segmentation network, the labeled and the unlabeled set. Due to the large-scale nature of semantic segmentation, it would be prohibitively expensive to compute features for each region in the unlabeled set at each step. Songming Liu received the bachelor’s degree in mechanical design and manufacturing and automation from Hefei University of Technology, Hefei, China, in 2017. 2 Search Log in; Search SpringerLink. As future work, we highlight the possibility of designing a better region definition, that could help improve the overall results, and adding domain adaptation for the learnt policy, to transfer it between datasets. In the first row, results for “full im.”, one entire image is labeled at each step (region size equal to the size of the image). This article is a comprehensive overview including a step-by-step guide to implement a deep learning image segmentation model. For every state st∈S (function of the segmentation network at timestep t), the agent can perform actions at∈A to choose which samples from Ut to annotate. Qualitative results in Cityscapes after running the active learning algorithm with a budget of 2k regions. Keywords: segmentation / Reinforcement learning / Deep Reinforcement / Supervised Lymph Node / weakly supervised lymph Scifeed alert for new publications Never miss any articles matching your research from any publisher (cropped from images in the original dataset) from a large unlabeled set to maximize the performance of a segmentation network f, parameterized by θ. 0 Looking at the big picture, semantic segmentation … To produce segmentation masks, key frames would go through both Nfeat and Ntask, while a fast feature interpolation method is used to obtain features for the non-key … For example, fully convolutional … Online ahead of print. Results with standard deviations in Table, P. Bachman, A. Sordoni, and A. Trischler (2017), V. Badrinarayanan, A. Kendall, and R. Cipolla (2017), Segnet: a deep convolutional encoder-decoder architecture for image segmentation, Online choice of active learning algorithms, A. Bearman, O. Russakovsky, V. Ferrari, and L. Fei-Fei (2016), What’s the point: semantic segmentation with point supervision, G. J. Brostow, J. Shotton, J. Fauqueur, and R. Cipolla (2008), Segmentation and recognition using structure from motion point clouds, R. Chan, M. Rottmann, F. Hüger, P. Schlicht, and H. Gottschalk (2019), Application of decision rules for handling class imbalance in semantic segmentation. ∙ maximizing performance of a segmentation model on a hold-out set. It overfits quickly to the training, getting a worst result that with the initial weights. He is a holder of the Alexander von Humboldt Research Fellowship of Germany, the JSPS Research Fellowship of Japan, William Mong Visiting Research Fellowship of Hong Kong. RL_segmentation. Empirically, our selector network is quite robust to the number of regions per step, as seen in Table E.3. The higher the entropy means closer to uniform distribution over classes, and our method has the highest entropy. Although label acquisition for semantic segmentation is more costly and time consuming than image classification, there has been considerably less work in active learning for semantic segmentation (Dutt Jain and Grauman, 2016; Mackowiak et al., 2018; Vezhnevets et al., 2012; Konyushkova et al., 2015; Gorriz et al., 2017; Yang et al., 2017), and they focus on hand-crafted strategies. AL for semantic segmentation. Original Research; Published: 27 March 2020; Multi-step medical image segmentation based on reinforcement learning. In Sener and Savarese (2018), they propose to select a batch of representative samples that maximize the coverage of the entire unlabeled set. Moreover, this dataset has the advantage of possessing the same categories as real datasets we experiment with.

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