July 28, 2019

3417 words 17 mins read

Paper Group ANR 402

Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks. DeadNet: Identifying Phototoxicity from Label-free Microscopy Images of Cells using Deep ConvNets. Objective evaluation metrics for automatic classification of EEG events. Gait Pattern Recognition Using Accelerometers. Subpopulation Diversity Based Selecting Migration Moment in …

Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks


Title	Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks
Authors	Vladimir Iglovikov, Alexander Rakhlin, Alexandr Kalinin, Alexey Shvets
Abstract	Skeletal bone age assessment is a common clinical practice to diagnose endocrine and metabolic disorders in child development. In this paper, we describe a fully automated deep learning approach to the problem of bone age assessment using data from Pediatric Bone Age Challenge organized by RSNA 2017. The dataset for this competition is consisted of 12.6k radiological images of left hand labeled by the bone age and sex of patients. Our approach utilizes several deep learning architectures: U-Net, ResNet-50, and custom VGG-style neural networks trained end-to-end. We use images of whole hands as well as specific parts of a hand for both training and inference. This approach allows us to measure importance of specific hand bones for the automated bone age analysis. We further evaluate performance of the method in the context of skeletal development stages. Our approach outperforms other common methods for bone age assessment.
Tasks
Published	2017-12-13
URL	http://arxiv.org/abs/1712.05053v2
PDF	http://arxiv.org/pdf/1712.05053v2.pdf
PWC	https://paperswithcode.com/paper/pediatric-bone-age-assessment-using-deep
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DeadNet: Identifying Phototoxicity from Label-free Microscopy Images of Cells using Deep ConvNets


Title	DeadNet: Identifying Phototoxicity from Label-free Microscopy Images of Cells using Deep ConvNets
Authors	David Richmond, Anna Payne-Tobin Jost, Talley Lambert, Jennifer Waters, Hunter Elliott
Abstract	Exposure to intense illumination light is an unavoidable consequence of fluorescence microscopy, and poses a risk to the health of the sample in every live-cell fluorescence microscopy experiment. Furthermore, the possible side-effects of phototoxicity on the scientific conclusions that are drawn from an imaging experiment are often unaccounted for. Previously, controlling for phototoxicity in imaging experiments required additional labels and experiments, limiting its widespread application. Here we provide a proof-of-principle demonstration that the phototoxic effects of an imaging experiment can be identified directly from a single phase-contrast image using deep convolutional neural networks (ConvNets). This lays the groundwork for an automated tool for assessing cell health in a wide range of imaging experiments. Interpretability of such a method is crucial for its adoption. We take steps towards interpreting the classification mechanism of the trained ConvNet by visualizing salient features of images that contribute to accurate classification.
Tasks
Published	2017-01-22
URL	http://arxiv.org/abs/1701.06109v1
PDF	http://arxiv.org/pdf/1701.06109v1.pdf
PWC	https://paperswithcode.com/paper/deadnet-identifying-phototoxicity-from-label
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Objective evaluation metrics for automatic classification of EEG events


Title	Objective evaluation metrics for automatic classification of EEG events
Authors	Saeedeh Ziyabari, Vinit Shah, Meysam Golmohammadi, Iyad Obeid, Joseph Picone
Abstract	The evaluation of machine learning algorithms in biomedical fields for applications involving sequential data lacks standardization. Common quantitative scalar evaluation metrics such as sensitivity and specificity can often be misleading depending on the requirements of the application. Evaluation metrics must ultimately reflect the needs of users yet be sufficiently sensitive to guide algorithm development. Feedback from critical care clinicians who use automated event detection software in clinical applications has been overwhelmingly emphatic that a low false alarm rate, typically measured in units of the number of errors per 24 hours, is the single most important criterion for user acceptance. Though using a single metric is not often as insightful as examining performance over a range of operating conditions, there is a need for a single scalar figure of merit. In this paper, we discuss the deficiencies of existing metrics for a seizure detection task and propose several new metrics that offer a more balanced view of performance. We demonstrate these metrics on a seizure detection task based on the TUH EEG Corpus. We show that two promising metrics are a measure based on a concept borrowed from the spoken term detection literature, Actual Term-Weighted Value (ATWV), and a new metric, Time-Aligned Event Scoring (TAES), that accounts for the temporal alignment of the hypothesis to the reference annotation. We also demonstrate that state of the art technology based on deep learning, though impressive in its performance, still needs significant improvement before it meets very strict user acceptance criteria.
Tasks	EEG, Seizure Detection
Published	2017-12-29
URL	https://arxiv.org/abs/1712.10107v3
PDF	https://arxiv.org/pdf/1712.10107v3.pdf
PWC	https://paperswithcode.com/paper/objective-evaluation-metrics-for-automatic
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Gait Pattern Recognition Using Accelerometers


Title	Gait Pattern Recognition Using Accelerometers
Authors	Vahid Alizadeh
Abstract	Motion ability is one of the most important human properties, including gait as a basis of human transitional movement. Gait, as a biometric for recognizing human identities, can be non-intrusively captured signals using wearable or portable smart devices. In this study gait patterns is collected using a wireless platform of two sensors located at chest and right ankle of the subjects. Then the raw data has undergone some preprocessing methods and segmented into 5 seconds windows. Some time and frequency domain features is extracted and the performance evaluated by 5 different classifiers. Decision Tree (with all features) and K-Nearest Neighbors (with 10 selected features) classifiers reached 99.4% and 100% respectively.
Tasks
Published	2017-03-11
URL	http://arxiv.org/abs/1703.03921v1
PDF	http://arxiv.org/pdf/1703.03921v1.pdf
PWC	https://paperswithcode.com/paper/gait-pattern-recognition-using-accelerometers
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Subpopulation Diversity Based Selecting Migration Moment in Distributed Evolutionary Algorithms


Title	Subpopulation Diversity Based Selecting Migration Moment in Distributed Evolutionary Algorithms
Authors	Chengjun Li, Jia Wu
Abstract	In distributed evolutionary algorithms, migration interval is used to decide migration moments. Nevertheless, migration moments predetermined by intervals cannot match the dynamic situation of evolution. In this paper, a scheme of setting the success rate of migration based on subpopulation diversity at each interval is proposed. With the scheme, migration still occurs at intervals, but the probability of immigrants entering the target subpopulation will be determined by the diversity of this subpopulation according to a proposed formula. An analysis shows that the time consumption of our scheme is acceptable. In our experiments, the basement of parallelism is an evolutionary algorithm for the traveling salesman problem. Under different value combinations of parameters for the formula, outcomes for eight benchmark instances of the distributed evolutionary algorithm with the proposed scheme are compared with those of a traditional one, respectively. Results show that the distributed evolutionary algorithm based on our scheme has a significant advantage on solutions especially for high difficulty instances. Moreover, it can be seen that the algorithm with the scheme has the most outstanding performance under three value combinations of above-mentioned parameters for the formula.
Tasks
Published	2017-01-05
URL	http://arxiv.org/abs/1701.01271v1
PDF	http://arxiv.org/pdf/1701.01271v1.pdf
PWC	https://paperswithcode.com/paper/subpopulation-diversity-based-selecting
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Calibration of Distributionally Robust Empirical Optimization Models


Title	Calibration of Distributionally Robust Empirical Optimization Models
Authors	Jun-Ya Gotoh, Michael Jong Kim, Andrew E. B. Lim
Abstract	In this paper, we study the out-of-sample properties of robust empirical optimization and develop a theory for data-driven calibration of the robustness parameter for worst-case maximization problems with concave reward functions. Building on the intuition that robust optimization reduces the sensitivity of the expected reward to errors in the model by controlling the spread of the reward distribution, we show that the first-order benefit of little bit of robustness is a significant reduction in the variance of the out-of-sample reward while the corresponding impact on the mean is almost an order of magnitude smaller. One implication is that a substantial reduction in the variance of the out-of-sample reward (i.e. sensitivity of the expected reward to model misspecification) is possible at little cost if the robustness parameter is properly calibrated. To this end, we introduce the notion of a robust mean-variance frontier to select the robustness parameter and show that it can be approximated using resampling methods like the bootstrap. Our examples also show that open loop calibration methods (e.g. selecting a 90% confidence level regardless of the data and objective function) can lead to solutions that are very conservative out-of-sample.
Tasks	Calibration
Published	2017-11-17
URL	http://arxiv.org/abs/1711.06565v1
PDF	http://arxiv.org/pdf/1711.06565v1.pdf
PWC	https://paperswithcode.com/paper/calibration-of-distributionally-robust
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Face Alignment Using K-Cluster Regression Forests With Weighted Splitting


Title	Face Alignment Using K-Cluster Regression Forests With Weighted Splitting
Authors	Marek Kowalski, Jacek Naruniec
Abstract	In this work we present a face alignment pipeline based on two novel methods: weighted splitting for K-cluster Regression Forests and 3D Affine Pose Regression for face shape initialization. Our face alignment method is based on the Local Binary Feature framework, where instead of standard regression forests and pixel difference features used in the original method, we use our K-cluster Regression Forests with Weighted Splitting (KRFWS) and Pyramid HOG features. We also use KRFWS to perform Affine Pose Regression (APR) and 3D-Affine Pose Regression (3D-APR), which intend to improve the face shape initialization. APR applies a rigid 2D transform to the initial face shape that compensates for inaccuracy in the initial face location, size and in-plane rotation. 3D-APR estimates the parameters of a 3D transform that additionally compensates for out-of-plane rotation. The resulting pipeline, consisting of APR and 3D-APR followed by face alignment, shows an improvement of 20% over standard LBF on the challenging IBUG dataset, and state-of-theart accuracy on the entire 300-W dataset.
Tasks	Face Alignment
Published	2017-06-06
URL	http://arxiv.org/abs/1706.01820v1
PDF	http://arxiv.org/pdf/1706.01820v1.pdf
PWC	https://paperswithcode.com/paper/face-alignment-using-k-cluster-regression
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Dense Optical Flow based Change Detection Network Robust to Difference of Camera Viewpoints


Title	Dense Optical Flow based Change Detection Network Robust to Difference of Camera Viewpoints
Authors	Ken Sakurada, Weimin Wang, Nobuo Kawaguchi, Ryosuke Nakamura
Abstract	This paper presents a novel method for detecting scene changes from a pair of images with a difference of camera viewpoints using a dense optical flow based change detection network. In the case that camera poses of input images are fixed or known, such as with surveillance and satellite cameras, the pixel correspondence between the images captured at different times can be known. Hence, it is possible to comparatively accurately detect scene changes between the images by modeling the appearance of the scene. On the other hand, in case of cameras mounted on a moving object, such as ground and aerial vehicles, we must consider the spatial correspondence between the images captured at different times. However, it can be difficult to accurately estimate the camera pose or 3D model of a scene, owing to the scene changes or lack of imagery. To solve this problem, we propose a change detection convolutional neural network utilizing dense optical flow between input images to improve the robustness to the difference between camera viewpoints. Our evaluation based on the panoramic change detection dataset shows that the proposed method outperforms state-of-the-art change detection algorithms.
Tasks	Optical Flow Estimation
Published	2017-12-08
URL	http://arxiv.org/abs/1712.02941v1
PDF	http://arxiv.org/pdf/1712.02941v1.pdf
PWC	https://paperswithcode.com/paper/dense-optical-flow-based-change-detection
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Constraint and Mathematical Programming Models for Integrated Port Container Terminal Operations


Title	Constraint and Mathematical Programming Models for Integrated Port Container Terminal Operations
Authors	Damla Kizilay, Deniz T. Eliiyi, Pascal Van Hentenryck
Abstract	This paper considers the integrated problem of quay crane assignment, quay crane scheduling, yard location assignment, and vehicle dispatching operations at a container terminal. The main objective is to minimize vessel turnover times and maximize the terminal throughput, which are key economic drivers in terminal operations. Due to their computational complexities, these problems are not optimized jointly in existing work. This paper revisits this limitation and proposes Mixed Integer Programming (MIP) and Constraint Programming (CP) models for the integrated problem, under some realistic assumptions. Experimental results show that the MIP formulation can only solve small instances, while the CP model finds optimal solutions in reasonable times for realistic instances derived from actual container terminal operations.
Tasks
Published	2017-12-14
URL	http://arxiv.org/abs/1712.05302v1
PDF	http://arxiv.org/pdf/1712.05302v1.pdf
PWC	https://paperswithcode.com/paper/constraint-and-mathematical-programming
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Uniform Inference for High-dimensional Quantile Regression: Linear Functionals and Regression Rank Scores


Title	Uniform Inference for High-dimensional Quantile Regression: Linear Functionals and Regression Rank Scores
Authors	Jelena Bradic, Mladen Kolar
Abstract	Hypothesis tests in models whose dimension far exceeds the sample size can be formulated much like the classical studentized tests only after the initial bias of estimation is removed successfully. The theory of debiased estimators can be developed in the context of quantile regression models for a fixed quantile value. However, it is frequently desirable to formulate tests based on the quantile regression process, as this leads to more robust tests and more stable confidence sets. Additionally, inference in quantile regression requires estimation of the so called sparsity function, which depends on the unknown density of the error. In this paper we consider a debiasing approach for the uniform testing problem. We develop high-dimensional regression rank scores and show how to use them to estimate the sparsity function, as well as how to adapt them for inference involving the quantile regression process. Furthermore, we develop a Kolmogorov-Smirnov test in a location-shift high-dimensional models and confidence sets that are uniformly valid for many quantile values. The main technical result are the development of a Bahadur representation of the debiasing estimator that is uniform over a range of quantiles and uniform convergence of the quantile process to the Brownian bridge process, which are of independent interest. Simulation studies illustrate finite sample properties of our procedure.
Tasks
Published	2017-02-20
URL	http://arxiv.org/abs/1702.06209v1
PDF	http://arxiv.org/pdf/1702.06209v1.pdf
PWC	https://paperswithcode.com/paper/uniform-inference-for-high-dimensional
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A Unified Framework for Long Range and Cold Start Forecasting of Seasonal Profiles in Time Series


Title	A Unified Framework for Long Range and Cold Start Forecasting of Seasonal Profiles in Time Series
Authors	Christopher Xie, Alex Tank, Alec Greaves-Tunnell, Emily Fox
Abstract	Providing long-range forecasts is a fundamental challenge in time series modeling, which is only compounded by the challenge of having to form such forecasts when a time series has never previously been observed. The latter challenge is the time series version of the cold-start problem seen in recommender systems which, to our knowledge, has not been addressed in previous work. A similar problem occurs when a long range forecast is required after only observing a small number of time points — a warm start forecast. With these aims in mind, we focus on forecasting seasonal profiles—or baseline demand—for periods on the order of a year in three cases: the long range case with multiple previously observed seasonal profiles, the cold start case with no previous observed seasonal profiles, and the warm start case with only a single partially observed profile. Classical time series approaches that perform iterated step-ahead forecasts based on previous observations struggle to provide accurate long range predictions; in settings with little to no observed data, such approaches are simply not applicable. Instead, we present a straightforward framework which combines ideas from high-dimensional regression and matrix factorization on a carefully constructed data matrix. Key to our formulation and resulting performance is leveraging (1) repeated patterns over fixed periods of time and across series, and (2) metadata associated with the individual series; without this additional data, the cold-start/warm-start problems are nearly impossible to solve. We demonstrate that our framework can accurately forecast an array of seasonal profiles on multiple large scale datasets.
Tasks	Recommendation Systems, Time Series
Published	2017-10-23
URL	http://arxiv.org/abs/1710.08473v2
PDF	http://arxiv.org/pdf/1710.08473v2.pdf
PWC	https://paperswithcode.com/paper/a-unified-framework-for-long-range-and-cold
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Beyond Keywords and Relevance: A Personalized Ad Retrieval Framework in E-Commerce Sponsored Search


Title	Beyond Keywords and Relevance: A Personalized Ad Retrieval Framework in E-Commerce Sponsored Search
Authors	Su Yan, Wei Lin, Tianshu Wu, Daorui Xiao, Xu Zheng, Bo Wu, Kaipeng Liu
Abstract	On most sponsored search platforms, advertisers bid on some keywords for their advertisements (ads). Given a search request, ad retrieval module rewrites the query into bidding keywords, and uses these keywords as keys to select Top N ads through inverted indexes. In this way, an ad will not be retrieved even if queries are related when the advertiser does not bid on corresponding keywords. Moreover, most ad retrieval approaches regard rewriting and ad-selecting as two separated tasks, and focus on boosting relevance between search queries and ads. Recently, in e-commerce sponsored search more and more personalized information has been introduced, such as user profiles, long-time and real-time clicks. Personalized information makes ad retrieval able to employ more elements (e.g. real-time clicks) as search signals and retrieval keys, however it makes ad retrieval more difficult to measure ads retrieved through different signals. To address these problems, we propose a novel ad retrieval framework beyond keywords and relevance in e-commerce sponsored search. Firstly, we employ historical ad click data to initialize a hierarchical network representing signals, keys and ads, in which personalized information is introduced. Then we train a model on top of the hierarchical network by learning the weights of edges. Finally we select the best edges according to the model, boosting RPM/CTR. Experimental results on our e-commerce platform demonstrate that our ad retrieval framework achieves good performance.
Tasks
Published	2017-12-29
URL	http://arxiv.org/abs/1712.10110v5
PDF	http://arxiv.org/pdf/1712.10110v5.pdf
PWC	https://paperswithcode.com/paper/beyond-keywords-and-relevance-a-personalized
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Discriminative and Geometry Aware Unsupervised Domain Adaptation


Title	Discriminative and Geometry Aware Unsupervised Domain Adaptation
Authors	Lingkun Luo, Liming Chen, Shiqiang Hu, Ying Lu, Xiaofang Wang
Abstract	Domain adaptation (DA) aims to generalize a learning model across training and testing data despite the mismatch of their data distributions. In light of a theoretical estimation of upper error bound, we argue in this paper that an effective DA method should 1) search a shared feature subspace where source and target data are not only aligned in terms of distributions as most state of the art DA methods do, but also discriminative in that instances of different classes are well separated; 2) account for the geometric structure of the underlying data manifold when inferring data labels on the target domain. In comparison with a baseline DA method which only cares about data distribution alignment between source and target, we derive three different DA models, namely CDDA, GA-DA, and DGA-DA, to highlight the contribution of Close yet Discriminative DA(CDDA) based on 1), Geometry Aware DA (GA-DA) based on 2), and finally Discriminative and Geometry Aware DA (DGA-DA) implementing jointly 1) and 2). Using both synthetic and real data, we show the effectiveness of the proposed approach which consistently outperforms state of the art DA methods over 36 image classification DA tasks through 6 popular benchmarks. We further carry out in-depth analysis of the proposed DA method in quantifying the contribution of each term of our DA model and provide insights into the proposed DA methods in visualizing both real and synthetic data.
Tasks	Domain Adaptation, Image Classification, Unsupervised Domain Adaptation
Published	2017-12-28
URL	http://arxiv.org/abs/1712.10042v1
PDF	http://arxiv.org/pdf/1712.10042v1.pdf
PWC	https://paperswithcode.com/paper/discriminative-and-geometry-aware
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Title	Multi-Modal Multi-Scale Deep Learning for Large-Scale Image Annotation
Authors	Yulei Niu, Zhiwu Lu, Ji-Rong Wen, Tao Xiang, Shih-Fu Chang
Abstract	Image annotation aims to annotate a given image with a variable number of class labels corresponding to diverse visual concepts. In this paper, we address two main issues in large-scale image annotation: 1) how to learn a rich feature representation suitable for predicting a diverse set of visual concepts ranging from object, scene to abstract concept; 2) how to annotate an image with the optimal number of class labels. To address the first issue, we propose a novel multi-scale deep model for extracting rich and discriminative features capable of representing a wide range of visual concepts. Specifically, a novel two-branch deep neural network architecture is proposed which comprises a very deep main network branch and a companion feature fusion network branch designed for fusing the multi-scale features computed from the main branch. The deep model is also made multi-modal by taking noisy user-provided tags as model input to complement the image input. For tackling the second issue, we introduce a label quantity prediction auxiliary task to the main label prediction task to explicitly estimate the optimal label number for a given image. Extensive experiments are carried out on two large-scale image annotation benchmark datasets and the results show that our method significantly outperforms the state-of-the-art.
Tasks
Published	2017-09-05
URL	http://arxiv.org/abs/1709.01220v2
PDF	http://arxiv.org/pdf/1709.01220v2.pdf
PWC	https://paperswithcode.com/paper/multi-modal-multi-scale-deep-learning-for
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The Multilinear Structure of ReLU Networks


Title	The Multilinear Structure of ReLU Networks
Authors	Thomas Laurent, James von Brecht
Abstract	We study the loss surface of neural networks equipped with a hinge loss criterion and ReLU or leaky ReLU nonlinearities. Any such network defines a piecewise multilinear form in parameter space. By appealing to harmonic analysis we show that all local minima of such network are non-differentiable, except for those minima that occur in a region of parameter space where the loss surface is perfectly flat. Non-differentiable minima are therefore not technicalities or pathologies; they are heart of the problem when investigating the loss of ReLU networks. As a consequence, we must employ techniques from nonsmooth analysis to study these loss surfaces. We show how to apply these techniques in some illustrative cases.
Tasks
Published	2017-12-29
URL	http://arxiv.org/abs/1712.10132v2
PDF	http://arxiv.org/pdf/1712.10132v2.pdf
PWC	https://paperswithcode.com/paper/the-multilinear-structure-of-relu-networks
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