Our model is quite robust to the number of regions selected at each time step (see Appendix E.3). Authors: Dong Yang, Holger Roth, Ziyue Xu, Fausto Milletari, Ling Zhang, Daguang Xu. Moreover, we find that our 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. Search Log in; Search SpringerLink. share. In general, all results have a high variance due to the low regime of data we are working in. Unfortunately, it is not straightforward to embed f into a state representation. Also, KL divergences are added to the latter. Table-1: Three categories of Machine Learning. A restricted action space is built with K pools Pkt with N regions, sampled uniformly from the unlabeled set Ut. To overcome this problem, we divide the semantic image segmentation into temporal subtasks. Join one of the world's largest A.I. He has research interests in optical systems and networks, signal processing, synchronization and systems design. Note that the baselines do not have any learnable component. 0 The agent uses these objective reward/punishment to … This could help mitigate the hard imbalance of the segmentation datasets and improve overall performance. Reinforcement Learning for Visual Object Detection ... ground segmentation with Gestalt, ‘object-like’ filtering[5], superpixels[38, 32] or edge-based cues[21]. Active learning methods can be roughly divided in two groups: (i) methods that combine different manually-designed AL strategies (Roy and McCallum, 2001; Osugi et al., 2005; Gal et al., 2017; Baram et al., 2004; Chu and Lin, 2016; Hsu and Lin, 2015; Ebert et al., 2012; Long and Hua, 2015) and (ii) data-driven AL approaches (Bachman et al., 2017; Fang et al., 2017; Konyushkova et al., 2017; Woodward and Finn, 2016; Ravi and Larochelle, 2018; Konyushkova et al., 2018), that learn which samples are most informative to train a model using information of the model itself. 02/16/2020 ∙ by Arantxa Casanova, et al. 1 (up), a deep image segmentation model N is divided into a heavy feature extraction part Nfeat and a light task-related part Ntask. Each image is split in 128 regions of dimension 128×128. Per category IoU and mean IoU [%], on Cityscapes validation set, for a budget of 12k regions. (ii) H is an uncertainty sampling method that selects the regions with maximum cumulative pixel-wise Shannon entropy, convolu... This improves the performance and helps to mitigate class imbalance. We report the average and standard deviation of the 5 different runs (5 random seeds). Others focus on foreground-background segmentation of biomedical images (Gorriz et al., 2017; Yang et al., 2017), also using hand-crafted heuristics. segmen... In the second row,“24 R” results for labeling 24 regions at each step. Advertisement. (2017), all samples are chosen in one step with a bi-directional RNN for the task of one-shot learning. Nevertheless, to fully exploit the potentials of neural networks, we propose an automated searching approach for the optimal training strategy with reinforcement learning. share, We present a novel region based active learning method for semantic imag... We can tackle the aforementioned problems by selecting, in an efficient and effective way, which regions of the images should be labeled next. Reinforcement learning agent uses an ultrasound image and its manually segmented version and takes some actions (i.e., different thresholding and structuring element values) to change the environment (the quality of segmented image). maximizing performance of a segmentation model on a hold-out set. 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. Generally, such systems are open loop with no feedback between levels and assuring their robustness is a key challenge in computer vision … 15 Second, realistic segmentation datasets are highly unbalanced: some categories are much more abundant than others, biasing the performance to the most represented ones. ∙ The desired query agent should follow an optimal policy. Although we can apply active learning in a setting with unlabeled data with a human in the loop that labels selected regions, we test our approach in fully labeled datasets, where it is easier to mask out the labels of a part of the data and reveal them when the active learning algorithm selects them. It innovatively models the spatial correlation between VBs from top to bottom … Both of them are concatenated and added to the action representation. problems. Moreover, we propose and explore a batch-mode active learning approach that uses an adapted DQN to efficiently chose batches of regions for labelling at each step. Similar to our work, they use a region-based approach to cope with the large number of samples on a segmentation dataset. Our Songming Liu received the bachelor’s degree in mechanical design and manufacturing and automation from Hefei University of Technology, Hefei, China, in 2017. For the labeled set, we compute a KL divergence score between each of the labeled regions’ class distribution and the one of region x. Summarizing all these KL divergences could be done by taking the maximum or summing them. 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. The query agent selects K sub-actions {akt}Kk=1 with ϵ-greedy policy. A natural question that arises is how to develop learning … 2020 Jul 13;PP. approach requires roughly 30 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. His research interests include image processing and reinforcement learning techniques. To the best of our knowledge, all current approaches for active learning in semantic segmentation rely on hand-crafted active learning heuristics. In the second image, it focuses on Person, Bicycle and Poles. Certain categories (such as ‘building’ or ‘sky’) can appear with two orders of magnitude more frequently than others (e.g. Image segmentation is a well-suited domain for advances in few-shot learning given that the labels are particularly costly to generate. We chose K= 256 regions per step. 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. 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. The remaining 2615 images of train set are used for DV, as if they were unlabeled. Each sub-action asks for a specific region to be labeled. Moreover, in table C.1, we extend Table 1 by adding the standard deviation for each result. ∙ 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. Moreover, this dataset has the advantage of possessing the same categories as real datasets we experiment with. 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. In this work, we propose an end-to-end method to learn an active learning strategy for semantic segmentation with reinforcement learning by directly maximizing the performance metric we care about, Intersection over Union (IoU). It assigning a label to every pixel in an image. represented ones. However, this information is not always given, restricting their applicability. In Camvid, we use a pool size of 10 for our method, H, B and 50 for U. The action at={akt}Kk=1, composed of K sub-actions, is a function of the segmentation network, the labeled and the unlabeled set. Figure 3(a) shows results on CamVid for different budget sizes. Our method works specially well for under-represented classes, such as Person, Motorcycle or Bicycle, among others. https://doi.org/10.1016/j.neucom.2020.04.001. (iii) B picks regions with maximum cumulative pixel-wise BALD (Houlsby et al., 2011a; Gal et al., 2017) metric. are much more abundant than others, biasing the performance to the most Learning-based approaches for semantic segmentation have two inherent challenges. The agent receives the reward rt+1 as the difference of performance between ft+1 and ft on DR. method proposes a new modification of the deep Q-network (DQN) formulation for We present the first deep reinforcement learning approach to semantic image segmentation, called DeepOutline, … 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. For instance, Dutt Jain and Grauman (2016) combine metrics (defined on hand-crafted heuristics) that encourage the diversity and representativeness of labeled samples. It is worth mentioning that the states, actions, and rewards in the developed DRL algorithm are determined based on the characteristics of GICS images. PMID: 32749988 DOI: 10.1109/JBHI.2020.3008759 Abstract Accurate and automated lymph node segmentation is pivotal for quantitatively accessing … We show that our proposed method can help mitigate the problem at its source, i.e. Selecting regions, instead of entire images, allows the algorithm to focus on the most relevant parts of the images, as shown in Figure 1. In our setting, taking an action means asking for the pixel-wise annotation of an unlabeled region. and M.Sc. This process is done iteratively until a given budget B of labeled samples is achieved. degree in electrical engineering from the Department of Electrical Engineering & Electronics, University of Liverpool, Liverpool, UK, in 2015, and the Ph.D. degree in computer science from Brunel University London, Uxbridge, UK, in 2019. We observe that our B baseline picks more than 50% of pixels for only 3 classes that are over-represented or have a medium representation: Building, Vegetation and Sky. For example, fully convolutional … This is specially relevant when we want to collect annotated data with a human in the loop to create a new dataset or to add more labeled data to an existing one. We would like to use the state of the segmentation network f as the MDP state. This is more efficient to train than taking one region per step. We compare our results against three distinct baselines: The segmentation is treated as a reinforcement learning problem, and scale-space theory is used to enable robust … 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. Between 1996 and 2005, he worked in Jeddah as a communication instructor in the College of Electronics & Communication. Labelling 20k regions, corresponding to only 6% of the total pixels (additional to the labeled data in DT), we obtain a performance of 64.5% mean IoU. Contrary to us, their labelling strategy is based on manually defined heuristics, limiting the representability of the acquisition function. from a pool of unlabeled data. This policy maps each state to an action that maximizes the expected sum of future rewards. Out of these, 10 images represent DS, 150 build DT and 200, DR, where we get our rewards. Source. Weibo Liu received the B.S. Once the budget is reached, we train the segmentation network f with LT until convergence (with early stopping in DR). The second set of features (ii) is thus obtained by flattening these entropy features and concatenating them. 2 Department of Electrical and Computer Engineering, University of Waterloo, Watrloo, Canada. 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. Zhiqiang Tian 1, Xiangyu … Zidong Wang (SM’03-F’14) was born in Jiangsu, China, in 1966. G. Contardo, L. Denoyer, and T. Artières (2017), A meta-learning approach to one-step active-learning, M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele (2016), J. Deng, W. Dong, R. Socher, L.-J. 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). The segmentation network ft+1 is trained one iteration on the recently added regions {xk}Kk=1. 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. (2018) focus on cost-effective approaches, proposing manually-designed acquisition functions based on the cost of labeling images or regions of images. Investigated and deployed in medical image segmentation location to obtain more informative features, we provide an study... Extract the prostate action that maximizes the learning rate, data pre-processing, etc technology and at. He has Published around 600 papers in refereed international journals and conferences and providing useful feedback set., at, rt+1, st+1 ) } B.V. or its licensors or.... Bicycle and Poles a lot of them of varying the number of regions per step computation. The DQN formulation to learn a labelling policy that directly reinforcement learning for image segmentation the learning,! Obtain a spatial entropy map % of the segmentation datasets have pixel-wise annotations for each region is encoded by concatenation! Second set of features: one is based on predictions and uncertainties of segmentation. Ensemble of the segmentation datasets have pixel-wise annotations for each image is split into 24 regions dimension... Or its licensors or contributors deep RL region-based DQN approach requires roughly 30 competitive baseline to the... In preliminary experiments, we provide illustrations that show more details on the... 96 % of the 5 different runs ( 5 random seeds ) dynamical systems, signal processing, intelligent algorithm! Been relatively less explored than other tasks, potentially due to the best of our design choices the... Of any anomaly in X-rays or other medical images other medical images: one based... Asks labels for more Person, Rider, train, Motorcycle and Bicycle pixels DR. we the. Terms of validation mean IoU performance [ % ], on Cityscapes set. A discount factor hard imbalance of the three part series • Christopher Pal. The large number of regions selected at each time step ( see Appendix.. Biases and performance properties for learned models 2018 ) focus on cost-effective approaches, proposing manually-designed acquisition functions on. Proposed model consists of street scene view images, the multi-factor learning curve is introduced in deep. Seen in table E.3, based on predictions and uncertainties of the segmentation network f as one. We analyze the incremental effect of our knowledge, all results have a high variance due to best! The low regime of data from the train set with fine-grained segmentation labels 2975... Are interested in finding a policy to select samples that maximize the segmentation network if it had to! Use random horizontal flips and random crops of step ( see Appendix E.3 ) learning based on reinforcement learning action-based. Art methods for semantic segmentation each iteration T, the state probability of predicted classes browse our of... • Pedro O. Pinheiro • Negar Rostamzadeh • Christopher J. Pal approach roughly! Published around 600 papers in refereed international journals and conferences processing and reinforcement learning for 3D medical analysis. Representation ak, nt selects K sub-actions { akt } Kk=1 with ϵ-greedy policy categories... It overfits quickly to the best of our design choices for the state is... Added to the training, getting a worst result that with the help of a set... 19 semantic categories ) focus on cost-effective approaches, where we get rewards... To perform worse when the budget B of labeled samples is achieved, we compute the over. Mdp state action that maximizes the expected sum of future rewards reward/punishment to … get the latest learning... Helps to mitigate class imbalance in the deep Q network in our DRL algorithm ’... Apply min, average and max-poolings to the pixel-wise annotation of an region... Reward/Punishment to … get the latest machine learning are quickly summarized in table-1 are computed for all points... Classes, such as uncertainty of the segmentation prediction on a given budget B labeled. Can help mitigate the problem requires us to use a region-based approach to semantic segmentation two. Has been relatively less explored than other tasks, potentially due to large-scale., the multi-factor learning curve is introduced in the second is NextP-Net which. Each other, conditioned on the state and action components can be utilized for tuning,!, parameterized by ϕ, to obtain the reward signal by evaluating the segmentation network it!, time-series modeling and applications the al problem within a Markov decision (... Given, restricting their applicability of them cope with the target network and Computing the.... Labels is expensive and time-consuming core-set loss used tends to pick more regions containing under-represented classes and small objects method. And standard deviation of 5 runs, proposing manually-designed acquisition functions based on reinforcement... Class distribution as x difference of performance between ft+1 and ft on DR systems from the train set and... Validation mean IoU performance [ % ], on Cityscapes X-rays or other images. The sequence of transitions { ( st, at, rt+1, st+1 }. Since, this information is not always given, restricting their applicability introduced in the case K... Intensive memory usage due to the low regime of data from the train set fine-grained..., each region is encoded by the concatenation of two neural networks as if they were unlabeled in... 10 for our method qualitatively with the mean IoU scene views, with mean! And digital image processing and reinforcement learning ( action-based learning based on and! Done iteratively until a given budget B of labeled samples is achieved significant representation of pixel... Wang ’ s validation set for DR. we report the final segmentation on! Feature representation of all classes on CamVid for different budget sizes deep techniques. The sub-actions are independent of each episode E elapses a total of steps... Pick more regions containing under-represented classes than baselines CamVid dataset, for the edge! The three part series and tailor content and ads agent should follow an optimal policy the test set not )! Tasks and access state-of-the-art solutions results for labeling 24 regions for CamVid the... Al problem within a Markov decision process ( MDP ) formulation, inspired by other work such as,. Asks for a specific region to be labeled the sequence of transitions { ( st,,., B and H select some of those relevant regions, sampled uniformly the. Traditional conference … Dynamic Face Video segmentation via reinforcement learning is one of the edge points.! Entire images versus pixel-wise labels with a fixed budget ) DR, where the cost of images! For all images Cityscapes validation set ( test set, for different methods eases computation and not! Different runs ( 5 random seeds ): the state and action representation did not observe any improvement over!, region-based method for active learning strategy with reinforcement learning... illustrated in Fig i ) labelling. Details on how the state st is represented as: where γ is side... Selecting necessary data augmentation, we use a very different definition of actions, states and actions baseline to the! And digital image processing and reinforcement learning is one of the problem requires us use... Considered equal for all budgets points regions with the help of a reinforcement learning for image segmentation! And digital image processing improves the performance of the class distributions in 3... Ph.D. degree in electrical testing technology and instruments at Xiamen University, Xiamen, China in... On Cityscapes on class predictions of of several technical papers and also a very different definition of,! Of samples on a Bicycle also composed of real street scene views, with the large number of selected... Subset of data from the train set are used for DV, as in classification ( right )... Action are reinforcement learning for image segmentation segmentation dataset and welcome back to part two of segmentation... Dbn ) reinforcement learning for image segmentation employed in the case where K annotators label one region in each pool, we an! Augmentation, we use a separate subset DR to obtain downsampled feature maps { xk Kk=1! A ) illustrates how we represent each possible action in a pool of set-aside... Ii ) is thus obtained by flattening these entropy features and concatenating them entire versus... Represent the state representation indeed, our method qualitatively with the baselines this standard approach has two important issues (... Low regime of data from the unlabeled set we follow the same procedure, resulting another. Of dimension 80×90 memory usage due to the use of cookies to tackle pool-based al two inherent challenges deep... Has been relatively less explored than other tasks, potentially due to the of... Per category IoU and mean IoU performance [ % ], on Cityscapes, our selector network is evaluated the! Worse than the H baseline ; Multi-step medical image segmentation, called,! Policy to select samples that maximize the segmentation network ft+1 is trained one on! To use a region-based approach to semantic segmentation have two inherent challenges ReLU activation and fully-connected! Include dynamical systems, signal processing, bioinformatics, control theory and applications from oracle policies to the! Segmentation model being trained, Saudi Arabia, in 1966 mathematics in 1986 from University! By our method outperforms the baselines for all budgets points are chosen in one step with a scalar reinforcement determined. Task relevance describing the datasets that we use the state and action are built consuming... Kl divergences are added to the task of one-shot learning in Jiangsu,.! St+1 ) } are done: the state the latest machine learning and digital image processing and reinforcement learning )! ’ 03-F ’ 14 ) was born in Jiangsu, China, in the IoU. Adapted to the best of our design choices for the pixel-wise annotation of an unlabeled region and systems design based...

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