April 2, 2020

3328 words 16 mins read

Paper Group ANR 97

Breast lesion segmentation in ultrasound images with limited annotated data. Organ Segmentation From Full-size CT Images Using Memory-Efficient FCN. Stochastic reconstruction of periodic, three-dimensional multi-phase electrode microstructures using generative adversarial networks. Neighborhood Structure Assisted Non-negative Matrix Factorization a …

Breast lesion segmentation in ultrasound images with limited annotated data


Title	Breast lesion segmentation in ultrasound images with limited annotated data
Authors	Bahareh Behboodi, Mina Amiri, Rupert Brooks, Hassan Rivaz
Abstract	Ultrasound (US) is one of the most commonly used imaging modalities in both diagnosis and surgical interventions due to its low-cost, safety, and non-invasive characteristic. US image segmentation is currently a unique challenge because of the presence of speckle noise. As manual segmentation requires considerable efforts and time, the development of automatic segmentation algorithms has attracted researchers attention. Although recent methodologies based on convolutional neural networks have shown promising performances, their success relies on the availability of a large number of training data, which is prohibitively difficult for many applications. Therefore, in this study we propose the use of simulated US images and natural images as auxiliary datasets in order to pre-train our segmentation network, and then to fine-tune with limited in vivo data. We show that with as little as 19 in vivo images, fine-tuning the pre-trained network improves the dice score by 21% compared to training from scratch. We also demonstrate that if the same number of natural and simulation US images is available, pre-training on simulation data is preferable.
Tasks	Lesion Segmentation, Semantic Segmentation
Published	2020-01-21
URL	https://arxiv.org/abs/2001.07322v1
PDF	https://arxiv.org/pdf/2001.07322v1.pdf
PWC	https://paperswithcode.com/paper/breast-lesion-segmentation-in-ultrasound
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Organ Segmentation From Full-size CT Images Using Memory-Efficient FCN


Title	Organ Segmentation From Full-size CT Images Using Memory-Efficient FCN
Authors	Chenglong Wang, Masahiro Oda, Kensaku Mori
Abstract	In this work, we present a memory-efficient fully convolutional network (FCN) incorporated with several memory-optimized techniques to reduce the run-time GPU memory demand during training phase. In medical image segmentation tasks, subvolume cropping has become a common preprocessing. Subvolumes (or small patch volumes) were cropped to reduce GPU memory demand. However, small patch volumes capture less spatial context that leads to lower accuracy. As a pilot study, the purpose of this work is to propose a memory-efficient FCN which enables us to train the model on full size CT image directly without subvolume cropping, while maintaining the segmentation accuracy. We optimize our network from both architecture and implementation. With the development of computing hardware, such as graphics processing unit (GPU) and tensor processing unit (TPU), now deep learning applications is able to train networks with large datasets within acceptable time. Among these applications, semantic segmentation using fully convolutional network (FCN) also has gained a significant improvement against traditional image processing approaches in both computer vision and medical image processing fields. However, unlike general color images used in computer vision tasks, medical images have larger scales than color images such as 3D computed tomography (CT) images, micro CT images, and histopathological images. For training these medical images, the large demand of computing resource become a severe problem. In this paper, we present a memory-efficient FCN to tackle the high GPU memory demand challenge in organ segmentation problem from clinical CT images. The experimental results demonstrated that our GPU memory demand is about 40% of baseline architecture, parameter amount is about 30% of the baseline.
Tasks	Computed Tomography (CT), Medical Image Segmentation, Semantic Segmentation
Published	2020-03-24
URL	https://arxiv.org/abs/2003.10690v1
PDF	https://arxiv.org/pdf/2003.10690v1.pdf
PWC	https://paperswithcode.com/paper/organ-segmentation-from-full-size-ct-images
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Stochastic reconstruction of periodic, three-dimensional multi-phase electrode microstructures using generative adversarial networks


Title	Stochastic reconstruction of periodic, three-dimensional multi-phase electrode microstructures using generative adversarial networks
Authors	Andrea Gayon-Lombardo, Lukas Mosser, Nigel P. Brandon, Samuel J. Cooper
Abstract	The generation of multiphase porous electrode microstructures is a critical step in the optimisation of electrochemical energy storage devices. This work implements a deep convolutional generative adversarial network (DC-GAN) for generating realistic n-phase microstructural data. The same network architecture is successfully applied to two very different three-phase microstructures: A lithium-ion battery cathode and a solid oxide fuel cell anode. A comparison between the real and synthetic data is performed in terms of the morphological properties (volume fraction, specific surface area, triple-phase boundary) and transport properties (relative diffusivity), as well as the two-point correlation function. The results show excellent agreement between for datasets and they are also visually indistinguishable. By modifying the input to the generator, we show that it is possible to generate microstructure with periodic boundaries in all three directions. This has the potential to significantly reduce the simulated volume required to be considered representative and therefore massively reduce the computational cost of the electrochemical simulations necessary to predict the performance of a particular microstructure during optimisation.
Tasks
Published	2020-02-17
URL	https://arxiv.org/abs/2003.11632v1
PDF	https://arxiv.org/pdf/2003.11632v1.pdf
PWC	https://paperswithcode.com/paper/stochastic-reconstruction-of-periodic-three
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Neighborhood Structure Assisted Non-negative Matrix Factorization and its Application in Unsupervised Point Anomaly Detection


Title	Neighborhood Structure Assisted Non-negative Matrix Factorization and its Application in Unsupervised Point Anomaly Detection
Authors	Imtiaz Ahmed, Xia Ben Hu, Mithun P. Acharya, Yu Ding
Abstract	Dimensionality reduction is considered as an important step for ensuring competitive performance in unsupervised learning such as anomaly detection. Non-negative matrix factorization (NMF) is a popular and widely used method to accomplish this goal. But NMF, together with its recent, enhanced version, like graph regularized NMF or symmetric NMF, do not have the provision to include the neighborhood structure information and, as a result, may fail to provide satisfactory performance in presence of nonlinear manifold structure. To address that shortcoming, we propose to consider and incorporate the neighborhood structural similarity information within the NMF framework by modeling the data through a minimum spanning tree. What motivates our choice is the understanding that in the presence of complicated data structure, a minimum spanning tree can approximate the intrinsic distance between two data points better than a simple Euclidean distance does, and consequently, it constitutes a more reasonable basis for differentiating anomalies from the normal class data. We label the resulting method as the neighborhood structure assisted NMF. By comparing the formulation and properties of the neighborhood structure assisted NMF with other versions of NMF including graph regularized NMF and symmetric NMF, it is apparent that the inclusion of the neighborhood structure information using minimum spanning tree makes a key difference. We further devise both offline and online algorithmic versions of the proposed method. Empirical comparisons using twenty benchmark datasets as well as an industrial dataset extracted from a hydropower plant demonstrate the superiority of the neighborhood structure assisted NMF and support our claim of merit.
Tasks	Anomaly Detection, Dimensionality Reduction
Published	2020-01-17
URL	https://arxiv.org/abs/2001.06541v1
PDF	https://arxiv.org/pdf/2001.06541v1.pdf
PWC	https://paperswithcode.com/paper/neighborhood-structure-assisted-non-negative
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Bootstrap Bias Corrected Cross Validation applied to Super Learning


Title	Bootstrap Bias Corrected Cross Validation applied to Super Learning
Authors	Krzysztof Mnich, Agnieszka Kitlas Golińska, Aneta Polewko-Klim, Witold R. Rudnicki
Abstract	Super learner algorithm can be applied to combine results of multiple base learners to improve quality of predictions. The default method for verification of super learner results is by nested cross validation. It has been proposed by Tsamardinos et al., that nested cross validation can be replaced by resampling for tuning hyper-parameters of the learning algorithms. We apply this idea to verification of super learner and compare with other verification methods, including nested cross validation. Tests were performed on artificial data sets of diverse size and on seven real, biomedical data sets. The resampling method, called Bootstrap Bias Correction, proved to be a reasonably precise and very cost-efficient alternative for nested cross validation.
Tasks
Published	2020-03-18
URL	https://arxiv.org/abs/2003.08342v1
PDF	https://arxiv.org/pdf/2003.08342v1.pdf
PWC	https://paperswithcode.com/paper/bootstrap-bias-corrected-cross-validation
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Survey of Personalization Techniques for Federated Learning


Title	Survey of Personalization Techniques for Federated Learning
Authors	Viraj Kulkarni, Milind Kulkarni, Aniruddha Pant
Abstract	Federated learning enables machine learning models to learn from private decentralized data without compromising privacy. The standard formulation of federated learning produces one shared model for all clients. Statistical heterogeneity due to non-IID distribution of data across devices often leads to scenarios where, for some clients, the local models trained solely on their private data perform better than the global shared model thus taking away their incentive to participate in the process. Several techniques have been proposed to personalize global models to work better for individual clients. This paper highlights the need for personalization and surveys recent research on this topic.
Tasks
Published	2020-03-19
URL	https://arxiv.org/abs/2003.08673v1
PDF	https://arxiv.org/pdf/2003.08673v1.pdf
PWC	https://paperswithcode.com/paper/survey-of-personalization-techniques-for
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Unsupervised Distribution Learning for Lunar Surface Anomaly Detection


Title	Unsupervised Distribution Learning for Lunar Surface Anomaly Detection
Authors	Adam Lesnikowski, Valentin T. Bickel, Daniel Angerhausen
Abstract	In this work we show that modern data-driven machine learning techniques can be successfully applied on lunar surface remote sensing data to learn, in an unsupervised way, sufficiently good representations of the data distribution to enable lunar technosignature and anomaly detection. In particular we train an unsupervised distribution learning neural network model to find the Apollo 15 landing module in a testing dataset, with no dataset specific model or hyperparameter tuning. Sufficiently good unsupervised data density estimation has the promise of enabling myriad useful downstream tasks, including locating lunar resources for future space flight and colonization, finding new impact craters or lunar surface reshaping, and algorithmically deciding the importance of unlabeled samples to send back from power- and bandwidth-constrained missions. We show in this work that such unsupervised learning can be successfully done in the lunar remote sensing and space science contexts.
Tasks	Anomaly Detection, Density Estimation
Published	2020-01-14
URL	https://arxiv.org/abs/2001.04634v1
PDF	https://arxiv.org/pdf/2001.04634v1.pdf
PWC	https://paperswithcode.com/paper/unsupervised-distribution-learning-for-lunar
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Stimulating Creativity with FunLines: A Case Study of Humor Generation in Headlines


Title	Stimulating Creativity with FunLines: A Case Study of Humor Generation in Headlines
Authors	Nabil Hossain, John Krumm, Tanvir Sajed, Henry Kautz
Abstract	Building datasets of creative text, such as humor, is quite challenging. We introduce FunLines, a competitive game where players edit news headlines to make them funny, and where they rate the funniness of headlines edited by others. FunLines makes the humor generation process fun, interactive, collaborative, rewarding and educational, keeping players engaged and providing humor data at a very low cost compared to traditional crowdsourcing approaches. FunLines offers useful performance feedback, assisting players in getting better over time at generating and assessing humor, as our analysis shows. This helps to further increase the quality of the generated dataset. We show the effectiveness of this data by training humor classification models that outperform a previous benchmark, and we release this dataset to the public.
Tasks
Published	2020-02-05
URL	https://arxiv.org/abs/2002.02031v1
PDF	https://arxiv.org/pdf/2002.02031v1.pdf
PWC	https://paperswithcode.com/paper/stimulating-creativity-with-funlines-a-case
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Framework

GhostImage: Perception Domain Attacks against Vision-based Object Classification Systems


Title	GhostImage: Perception Domain Attacks against Vision-based Object Classification Systems
Authors	Yanmao Man, Ming Li, Ryan Gerdes
Abstract	In vision-based object classification systems, imaging sensors perceive the environment and then objects are detected and classified for decision-making purposes. Vulnerabilities in the perception domain enable an attacker to inject false data into the sensor which could lead to unsafe consequences. In this work, we focus on camera-based systems and propose GhostImage attacks, with the goal of either creating a fake perceived object or obfuscating the object’s image that leads to wrong classification results. This is achieved by remotely projecting adversarial patterns into camera-perceived images, exploiting two common effects in optical imaging systems, namely lens flare/ghost effects, and auto-exposure control. To improve the robustness of the attack to channel perturbations, we generate optimal input patterns by integrating adversarial machine learning techniques with a trained end-to-end channel model. We realize GhostImage attacks with a projector, and conducted comprehensive experiments, using three different image datasets, in indoor and outdoor environments, and three different cameras. We demonstrate that GhostImage attacks are applicable to both autonomous driving and security surveillance scenarios. Experiment results show that, depending on the projector-camera distance, attack success rates can reach as high as 100%.
Tasks	Autonomous Driving, Decision Making, Object Classification
Published	2020-01-21
URL	https://arxiv.org/abs/2001.07792v1
PDF	https://arxiv.org/pdf/2001.07792v1.pdf
PWC	https://paperswithcode.com/paper/ghostimage-perception-domain-attacks-against
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Simulation Assisted Likelihood-free Anomaly Detection


Title	Simulation Assisted Likelihood-free Anomaly Detection
Authors	Anders Andreassen, Benjamin Nachman, David Shih
Abstract	Given the lack of evidence for new particle discoveries at the Large Hadron Collider (LHC), it is critical to broaden the search program. A variety of model-independent searches have been proposed, adding sensitivity to unexpected signals. There are generally two types of such searches: those that rely heavily on simulations and those that are entirely based on (unlabeled) data. This paper introduces a hybrid method that makes the best of both approaches. For potential signals that are resonant in one known feature, this new method first learns a parameterized reweighting function to morph a given simulation to match the data in sidebands. This function is then interpolated into the signal region and then the reweighted background-only simulation can be used for supervised learning as well as for background estimation. The background estimation from the reweighted simulation allows for non-trivial correlations between features used for classification and the resonant feature. A dijet search with jet substructure is used to illustrate the new method. Future applications of Simulation Assisted Likelihood-free Anomaly Detection (SALAD) include a variety of final states and potential combinations with other model-independent approaches.
Tasks	Anomaly Detection
Published	2020-01-14
URL	https://arxiv.org/abs/2001.05001v1
PDF	https://arxiv.org/pdf/2001.05001v1.pdf
PWC	https://paperswithcode.com/paper/simulation-assisted-likelihood-free-anomaly
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Framework

A macro agent and its actions


Title	A macro agent and its actions
Authors	Larissa Albantakis, Francesco Massari, Maggie Beheler-Amass, Giulio Tononi
Abstract	In science, macro level descriptions of the causal interactions within complex, dynamical systems are typically deemed convenient, but ultimately reducible to a complete causal account of the underlying micro constituents. Yet, such a reductionist perspective is hard to square with several issues related to autonomy and agency: (1) agents require (causal) borders that separate them from the environment, (2) at least in a biological context, agents are associated with macroscopic systems, and (3) agents are supposed to act upon their environment. Integrated information theory (IIT) (Oizumi et al., 2014) offers a quantitative account of causation based on a set of causal principles, including notions such as causal specificity, composition, and irreducibility, that challenges the reductionist perspective in multiple ways. First, the IIT formalism provides a complete account of a system’s causal structure, including irreducible higher-order mechanisms constituted of multiple system elements. Second, a system’s amount of integrated information ($\Phi$) measures the causal constraints a system exerts onto itself and can peak at a macro level of description (Hoel et al., 2016; Marshall et al., 2018). Finally, the causal principles of IIT can also be employed to identify and quantify the actual causes of events (“what caused what”), such as an agent’s actions (Albantakis et al., 2019). Here, we demonstrate this framework by example of a simulated agent, equipped with a small neural network, that forms a maximum of $\Phi$ at a macro scale.
Tasks
Published	2020-03-31
URL	https://arxiv.org/abs/2004.00058v1
PDF	https://arxiv.org/pdf/2004.00058v1.pdf
PWC	https://paperswithcode.com/paper/a-macro-agent-and-its-actions
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Framework

Regularized Submodular Maximization at Scale


Title	Regularized Submodular Maximization at Scale
Authors	Ehsan Kazemi, Shervin Minaee, Moran Feldman, Amin Karbasi
Abstract	In this paper, we propose scalable methods for maximizing a regularized submodular function $f = g - \ell$ expressed as the difference between a monotone submodular function $g$ and a modular function $\ell$. Indeed, submodularity is inherently related to the notions of diversity, coverage, and representativeness. In particular, finding the mode of many popular probabilistic models of diversity, such as determinantal point processes, submodular probabilistic models, and strongly log-concave distributions, involves maximization of (regularized) submodular functions. Since a regularized function $f$ can potentially take on negative values, the classic theory of submodular maximization, which heavily relies on the non-negativity assumption of submodular functions, may not be applicable. To circumvent this challenge, we develop the first one-pass streaming algorithm for maximizing a regularized submodular function subject to a $k$-cardinality constraint. It returns a solution $S$ with the guarantee that $f(S)\geq(\phi^{-2}-\epsilon) \cdot g(OPT)-\ell (OPT)$, where $\phi$ is the golden ratio. Furthermore, we develop the first distributed algorithm that returns a solution $S$ with the guarantee that $\mathbb{E}[f(S)] \geq (1-\epsilon) [(1-e^{-1}) \cdot g(OPT)-\ell(OPT)]$ in $O(1/ \epsilon)$ rounds of MapReduce computation, without keeping multiple copies of the entire dataset in each round (as it is usually done). We should highlight that our result, even for the unregularized case where the modular term $\ell$ is zero, improves the memory and communication complexity of the existing work by a factor of $O(1/ \epsilon)$ while arguably provides a simpler distributed algorithm and a unifying analysis. We also empirically study the performance of our scalable methods on a set of real-life applications, including finding the mode of distributions, data summarization, and product recommendation.
Tasks	Data Summarization, Point Processes, Product Recommendation
Published	2020-02-10
URL	https://arxiv.org/abs/2002.03503v1
PDF	https://arxiv.org/pdf/2002.03503v1.pdf
PWC	https://paperswithcode.com/paper/regularized-submodular-maximization-at-scale
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Semi-analytic approximate stability selection for correlated data in generalized linear models


Title	Semi-analytic approximate stability selection for correlated data in generalized linear models
Authors	Takashi Takahashi, Yoshiyuki Kabashima
Abstract	We consider the variable selection problem of generalized linear models (GLMs). Stability selection (SS) is a promising method proposed for solving this problem. Although SS provides practical variable selection criteria, it is computationally demanding because it needs to fit GLMs to many re-sampled datasets. We propose a novel approximate inference algorithm that can conduct SS without the repeated fitting. The algorithm is based on the replica method of statistical mechanics and vector approximate message passing of information theory. For datasets characterized by rotation-invariant matrix ensembles, we derive state evolution equations that macroscopically describe the dynamics of the proposed algorithm. We also show that their fixed points are consistent with the replica symmetric solution obtained by the replica method. Numerical experiments indicate that the algorithm exhibits fast convergence and high approximation accuracy for both synthetic and real-world data.
Tasks
Published	2020-03-19
URL	https://arxiv.org/abs/2003.08670v1
PDF	https://arxiv.org/pdf/2003.08670v1.pdf
PWC	https://paperswithcode.com/paper/semi-analytic-approximate-stability-selection
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Framework

A semi-supervised sparse K-Means algorithm


Title	A semi-supervised sparse K-Means algorithm
Authors	Avgoustinos Vouros, Eleni Vasilaki
Abstract	We consider the problem of data clustering with unidentified feature quality but the existence of small amount of label data. In the first case a sparse clustering method can be employed in order to detect the subgroup of features necessary for clustering and in the second case a semi-supervised method can use the labelled data to create constraints and enhance the clustering solution. In this paper we propose a K-Means inspired algorithm that employs these techniques. We show that the algorithm maintains the high performance of other similar semi-supervised algorthms as well as keeping the ability to identify informative from uninformative features. We examine the performance of the algorithm on real world data sets with unknown features quality as well as a real world data set with a known uninformative feature. We use a series of scenarios with different number and types of constraints.
Tasks
Published	2020-03-16
URL	https://arxiv.org/abs/2003.06973v2
PDF	https://arxiv.org/pdf/2003.06973v2.pdf
PWC	https://paperswithcode.com/paper/a-semi-supervised-sparse-k-means-algorithm
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Stochastic Optimization for Regularized Wasserstein Estimators


Title	Stochastic Optimization for Regularized Wasserstein Estimators
Authors	Marin Ballu, Quentin Berthet, Francis Bach
Abstract	Optimal transport is a foundational problem in optimization, that allows to compare probability distributions while taking into account geometric aspects. Its optimal objective value, the Wasserstein distance, provides an important loss between distributions that has been used in many applications throughout machine learning and statistics. Recent algorithmic progress on this problem and its regularized versions have made these tools increasingly popular. However, existing techniques require solving an optimization problem to obtain a single gradient of the loss, thus slowing down first-order methods to minimize the sum of losses, that require many such gradient computations. In this work, we introduce an algorithm to solve a regularized version of this problem of Wasserstein estimators, with a time per step which is sublinear in the natural dimensions of the problem. We introduce a dual formulation, and optimize it with stochastic gradient steps that can be computed directly from samples, without solving additional optimization problems at each step. Doing so, the estimation and computation tasks are performed jointly. We show that this algorithm can be extended to other tasks, including estimation of Wasserstein barycenters. We provide theoretical guarantees and illustrate the performance of our algorithm with experiments on synthetic data.
Tasks	Stochastic Optimization
Published	2020-02-20
URL	https://arxiv.org/abs/2002.08695v1
PDF	https://arxiv.org/pdf/2002.08695v1.pdf
PWC	https://paperswithcode.com/paper/stochastic-optimization-for-regularized
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