October 16, 2019

3250 words 16 mins read

Paper Group ANR 1007

Improving Breast Cancer Detection using Symmetry Information with Deep Learning. Beyond Structural Causal Models: Causal Constraints Models. Score Permutation Based Finite Sample Inference for Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) Models. 3D Topology Optimization using Convolutional Neural Networks. 3D Global Convolution …

Improving Breast Cancer Detection using Symmetry Information with Deep Learning


Title	Improving Breast Cancer Detection using Symmetry Information with Deep Learning
Authors	Yeman Brhane Hagos, Albert Gubern Merida, Jonas Teuwen
Abstract	Convolutional Neural Networks (CNN) have had a huge success in many areas of computer vision and medical image analysis. However, there is still an immense potential for performance improvement in mammogram breast cancer detection Computer-Aided Detection (CAD) systems by integrating all the information that the radiologist utilizes, such as symmetry and temporal data. In this work, we proposed a patch based multi-input CNN that learns symmetrical difference to detect breast masses. The network was trained on a large-scale dataset of 28294 mammogram images. The performance was compared to a baseline architecture without symmetry context using Area Under the ROC Curve (AUC) and Competition Performance Metric (CPM). At candidate level, AUC value of 0.933 with 95% confidence interval of [0.920, 0.954] was obtained when symmetry information is incorporated in comparison with baseline architecture which yielded AUC value of 0.929 with [0.919, 0.947] confidence interval. By incorporating symmetrical information, although there was no a significant candidate level performance again (p = 0.111), we have found a compelling result at exam level with CPM value of 0.733 (p = 0.001). We believe that including temporal data, and adding benign class to the dataset could improve the detection performance.
Tasks	Breast Cancer Detection
Published	2018-08-17
URL	http://arxiv.org/abs/1808.08273v1
PDF	http://arxiv.org/pdf/1808.08273v1.pdf
PWC	https://paperswithcode.com/paper/improving-breast-cancer-detection-using
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Beyond Structural Causal Models: Causal Constraints Models


Title	Beyond Structural Causal Models: Causal Constraints Models
Authors	Tineke Blom, Stephan Bongers, Joris M. Mooij
Abstract	Structural Causal Models (SCMs) provide a popular causal modeling framework. In this work, we show that SCMs are not flexible enough to give a complete causal representation of dynamical systems at equilibrium. Instead, we propose a generalization of the notion of an SCM, that we call Causal Constraints Model (CCM), and prove that CCMs do capture the causal semantics of such systems. We show how CCMs can be constructed from differential equations and initial conditions and we illustrate our ideas further on a simple but ubiquitous (bio)chemical reaction. Our framework also allows to model functional laws, such as the ideal gas law, in a sensible and intuitive way.
Tasks
Published	2018-05-16
URL	https://arxiv.org/abs/1805.06539v3
PDF	https://arxiv.org/pdf/1805.06539v3.pdf
PWC	https://paperswithcode.com/paper/generalized-strucutral-causal-models
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Score Permutation Based Finite Sample Inference for Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) Models


Title	Score Permutation Based Finite Sample Inference for Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) Models
Authors	Balázs Csanád Csáji
Abstract	A standard model of (conditional) heteroscedasticity, i.e., the phenomenon that the variance of a process changes over time, is the Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model, which is especially important for economics and finance. GARCH models are typically estimated by the Quasi-Maximum Likelihood (QML) method, which works under mild statistical assumptions. Here, we suggest a finite sample approach, called ScoPe, to construct distribution-free confidence regions around the QML estimate, which have exact coverage probabilities, despite no additional assumptions about moments are made. ScoPe is inspired by the recently developed Sign-Perturbed Sums (SPS) method, which however cannot be applied in the GARCH case. ScoPe works by perturbing the score function using randomly permuted residuals. This produces alternative samples which lead to exact confidence regions. Experiments on simulated and stock market data are also presented, and ScoPe is compared with the asymptotic theory and bootstrap approaches.
Tasks
Published	2018-07-23
URL	http://arxiv.org/abs/1807.08390v1
PDF	http://arxiv.org/pdf/1807.08390v1.pdf
PWC	https://paperswithcode.com/paper/score-permutation-based-finite-sample
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3D Topology Optimization using Convolutional Neural Networks


Title	3D Topology Optimization using Convolutional Neural Networks
Authors	Saurabh Banga, Harsh Gehani, Sanket Bhilare, Sagar Patel, Levent Kara
Abstract	Topology optimization is computationally demanding that requires the assembly and solution to a finite element problem for each material distribution hypothesis. As a complementary alternative to the traditional physics-based topology optimization, we explore a data-driven approach that can quickly generate accurate solutions. To this end, we propose a deep learning approach based on a 3D encoder-decoder Convolutional Neural Network architecture for accelerating 3D topology optimization and to determine the optimal computational strategy for its deployment. Analysis of iteration-wise progress of the Solid Isotropic Material with Penalization process is used as a guideline to study how the earlier steps of the conventional topology optimization can be used as input for our approach to predict the final optimized output structure directly from this input. We conduct a comparative study between multiple strategies for training the neural network and assess the effect of using various input combinations for the CNN to finalize the strategy with the highest accuracy in predictions for practical deployment. For the best performing network, we achieved about 40% reduction in overall computation time while also attaining structural accuracies in the order of 96%.
Tasks
Published	2018-08-22
URL	http://arxiv.org/abs/1808.07440v1
PDF	http://arxiv.org/pdf/1808.07440v1.pdf
PWC	https://paperswithcode.com/paper/3d-topology-optimization-using-convolutional
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3D Global Convolutional Adversarial Network\ for Prostate MR Volume Segmentation


Title	3D Global Convolutional Adversarial Network\ for Prostate MR Volume Segmentation
Authors	Haozhe Jia, Yang Song, Donghao Zhang, Heng Huang, Dagan Feng, Michael Fulham, Yong Xia, Weidong Cai
Abstract	Advanced deep learning methods have been developed to conduct prostate MR volume segmentation in either a 2D or 3D fully convolutional manner. However, 2D methods tend to have limited segmentation performance, since large amounts of spatial information of prostate volumes are discarded during the slice-by-slice segmentation process; and 3D methods also have room for improvement, since they use isotropic kernels to perform 3D convolutions whereas most prostate MR volumes have anisotropic spatial resolution. Besides, the fully convolutional structural methods achieve good performance for localization issues but neglect the per-voxel classification for segmentation tasks. In this paper, we propose a 3D Global Convolutional Adversarial Network (3D GCA-Net) to address efficient prostate MR volume segmentation. We first design a 3D ResNet encoder to extract 3D features from prostate scans, and then develop the decoder, which is composed of a multi-scale 3D global convolutional block and a 3D boundary refinement block, to address the classification and localization issues simultaneously for volumetric segmentation. Additionally, we combine the encoder-decoder segmentation network with an adversarial network in the training phrase to enforce the contiguity of long-range spatial predictions. Throughout the proposed model, we use anisotropic convolutional processing for better feature learning on prostate MR scans. We evaluated our 3D GCA-Net model on two public prostate MR datasets and achieved state-of-the-art performances.
Tasks
Published	2018-07-18
URL	http://arxiv.org/abs/1807.06742v1
PDF	http://arxiv.org/pdf/1807.06742v1.pdf
PWC	https://paperswithcode.com/paper/3d-global-convolutional-adversarial-network
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Framework

Automatic Assessment of Full Left Ventricular Coverage in Cardiac Cine Magnetic Resonance Imaging with Fisher-Discriminative 3D CNN


Title	Automatic Assessment of Full Left Ventricular Coverage in Cardiac Cine Magnetic Resonance Imaging with Fisher-Discriminative 3D CNN
Authors	Le Zhang, Ali Gooya, Marco Pereanez, Bo Dong, Stefan K. Piechnik, Stefan Neubauer, Steffen E. Petersen, Alejandro F. Frangi
Abstract	Cardiac magnetic resonance (CMR) images play a growing role in the diagnostic imaging of cardiovascular diseases. Full coverage of the left ventricle (LV), from base to apex, is a basic criterion for CMR image quality and necessary for accurate measurement of cardiac volume and functional assessment. Incomplete coverage of the LV is identified through visual inspection, which is time-consuming and usually done retrospectively in the assessment of large imaging cohorts. This paper proposes a novel automatic method for determining LV coverage from CMR images by using Fisher-discriminative three-dimensional (FD3D) convolutional neural networks (CNNs). In contrast to our previous method employing 2D CNNs, this approach utilizes spatial contextual information in CMR volumes, extracts more representative high-level features and enhances the discriminative capacity of the baseline 2D CNN learning framework, thus achieving superior detection accuracy. A two-stage framework is proposed to identify missing basal and apical slices in measurements of CMR volume. First, the FD3D CNN extracts high-level features from the CMR stacks. These image representations are then used to detect the missing basal and apical slices. Compared to the traditional 3D CNN strategy, the proposed FD3D CNN minimizes within-class scatter and maximizes between-class scatter. We performed extensive experiments to validate the proposed method on more than 5,000 independent volumetric CMR scans from the UK Biobank study, achieving low error rates for missing basal/apical slice detection (4.9%/4.6%). The proposed method can also be adopted for assessing LV coverage for other types of CMR image data.
Tasks
Published	2018-11-06
URL	http://arxiv.org/abs/1811.02688v2
PDF	http://arxiv.org/pdf/1811.02688v2.pdf
PWC	https://paperswithcode.com/paper/automatic-assessment-of-full-left-ventricular
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Synergistic Drug Combination Prediction by Integrating Multi-omics Data in Deep Learning Models


Title	Synergistic Drug Combination Prediction by Integrating Multi-omics Data in Deep Learning Models
Authors	Tianyu Zhang, Liwei Zhang, Philip R. O. Payne, Fuhai Li
Abstract	Drug resistance is still a major challenge in cancer therapy. Drug combination is expected to overcome drug resistance. However, the number of possible drug combinations is enormous, and thus it is infeasible to experimentally screen all effective drug combinations considering the limited resources. Therefore, computational models to predict and prioritize effective drug combinations is important for combinatory therapy discovery in cancer. In this study, we proposed a novel deep learning model, AuDNNsynergy, to prediction drug combinations by integrating multi-omics data and chemical structure data. In specific, three autoencoders were trained using the gene expression, copy number and genetic mutation data of all tumor samples from The Cancer Genome Atlas. Then the physicochemical properties of drugs combined with the output of the three autoencoders, characterizing the individual cancer cell-lines, were used as the input of a deep neural network that predicts the synergy value of given pair-wise drug combinations against the specific cancer cell-lines. The comparison results showed the proposed AuDNNsynergy model outperforms four state-of-art approaches, namely DeepSynergy, Gradient Boosting Machines, Random Forests, and Elastic Nets. Moreover, we conducted the interpretation analysis of the deep learning model to investigate potential vital genetic predictors and the underlying mechanism of synergistic drug combinations on specific cancer cell-lines.
Tasks
Published	2018-11-16
URL	http://arxiv.org/abs/1811.07054v1
PDF	http://arxiv.org/pdf/1811.07054v1.pdf
PWC	https://paperswithcode.com/paper/synergistic-drug-combination-prediction-by
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Framework

End-to-End Saliency Mapping via Probability Distribution Prediction


Title	End-to-End Saliency Mapping via Probability Distribution Prediction
Authors	Saumya Jetley, Naila Murray, Eleonora Vig
Abstract	Most saliency estimation methods aim to explicitly model low-level conspicuity cues such as edges or blobs and may additionally incorporate top-down cues using face or text detection. Data-driven methods for training saliency models using eye-fixation data are increasingly popular, particularly with the introduction of large-scale datasets and deep architectures. However, current methods in this latter paradigm use loss functions designed for classification or regression tasks whereas saliency estimation is evaluated on topographical maps. In this work, we introduce a new saliency map model which formulates a map as a generalized Bernoulli distribution. We then train a deep architecture to predict such maps using novel loss functions which pair the softmax activation function with measures designed to compute distances between probability distributions. We show in extensive experiments the effectiveness of such loss functions over standard ones on four public benchmark datasets, and demonstrate improved performance over state-of-the-art saliency methods.
Tasks	Saliency Prediction
Published	2018-04-05
URL	http://arxiv.org/abs/1804.01793v1
PDF	http://arxiv.org/pdf/1804.01793v1.pdf
PWC	https://paperswithcode.com/paper/end-to-end-saliency-mapping-via-probability
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Framework

Approximate Collapsed Gibbs Clustering with Expectation Propagation


Title	Approximate Collapsed Gibbs Clustering with Expectation Propagation
Authors	Christopher Aicher, Emily B. Fox
Abstract	We develop a framework for approximating collapsed Gibbs sampling in generative latent variable cluster models. Collapsed Gibbs is a popular MCMC method, which integrates out variables in the posterior to improve mixing. Unfortunately for many complex models, integrating out these variables is either analytically or computationally intractable. We efficiently approximate the necessary collapsed Gibbs integrals by borrowing ideas from expectation propagation. We present two case studies where exact collapsed Gibbs sampling is intractable: mixtures of Student-t’s and time series clustering. Our experiments on real and synthetic data show that our approximate sampler enables a runtime-accuracy tradeoff in sampling these types of models, providing results with competitive accuracy much more rapidly than the naive Gibbs samplers one would otherwise rely on in these scenarios.
Tasks	Time Series, Time Series Clustering
Published	2018-07-19
URL	http://arxiv.org/abs/1807.07621v1
PDF	http://arxiv.org/pdf/1807.07621v1.pdf
PWC	https://paperswithcode.com/paper/approximate-collapsed-gibbs-clustering-with
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A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer


Title	A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer
Authors	Milad Zafar Nezhad, Najibesadat Sadati, Kai Yang, Dongxiao Zhu
Abstract	Survival analysis has been developed and applied in the number of areas including manufacturing, finance, economics and healthcare. In healthcare domain, usually clinical data are high-dimensional, sparse and complex and sometimes there exists few amount of time-to-event (labeled) instances. Therefore building an accurate survival model from electronic health records is challenging. With this motivation, we address this issue and provide a new survival analysis framework using deep learning and active learning with a novel sampling strategy. First, our approach provides better representation with lower dimensions from clinical features using labeled (time-to-event) and unlabeled (censored) instances and then actively trains the survival model by labeling the censored data using an oracle. As a clinical assistive tool, we introduce a simple effective treatment recommendation approach based on our survival model. In the experimental study, we apply our approach on SEER-Medicare data related to prostate cancer among African-Americans and white patients. The results indicate that our approach outperforms significantly than baseline models.
Tasks	Active Learning, Survival Analysis
Published	2018-04-10
URL	http://arxiv.org/abs/1804.03280v1
PDF	http://arxiv.org/pdf/1804.03280v1.pdf
PWC	https://paperswithcode.com/paper/a-deep-active-survival-analysis-approach-for
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Framework

Task Specific Visual Saliency Prediction with Memory Augmented Conditional Generative Adversarial Networks


Title	Task Specific Visual Saliency Prediction with Memory Augmented Conditional Generative Adversarial Networks
Authors	Tharindu Fernando, Simon Denman, Sridha Sridharan, Clinton Fookes
Abstract	Visual saliency patterns are the result of a variety of factors aside from the image being parsed, however existing approaches have ignored these. To address this limitation, we propose a novel saliency estimation model which leverages the semantic modelling power of conditional generative adversarial networks together with memory architectures which capture the subject’s behavioural patterns and task dependent factors. We make contributions aiming to bridge the gap between bottom-up feature learning capabilities in modern deep learning architectures and traditional top-down hand-crafted features based methods for task specific saliency modelling. The conditional nature of the proposed framework enables us to learn contextual semantics and relationships among different tasks together, instead of learning them separately for each task. Our studies not only shed light on a novel application area for generative adversarial networks, but also emphasise the importance of task specific saliency modelling and demonstrate the plausibility of fully capturing this context via an augmented memory architecture.
Tasks	Saliency Prediction
Published	2018-03-09
URL	http://arxiv.org/abs/1803.03354v1
PDF	http://arxiv.org/pdf/1803.03354v1.pdf
PWC	https://paperswithcode.com/paper/task-specific-visual-saliency-prediction-with
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Continual Match Based Training in Pommerman: Technical Report


Title	Continual Match Based Training in Pommerman: Technical Report
Authors	Peng Peng, Liang Pang, Yufeng Yuan, Chao Gao
Abstract	Continual learning is the ability of agents to improve their capacities throughout multiple tasks continually. While recent works in the literature of continual learning mostly focused on developing either particular loss functions or specialized structures of neural network explaining the episodic memory or neural plasticity, we study continual learning from the perspective of the training mechanism. Specifically, we propose a COnitnual Match BAsed Training (COMBAT) framework for training a population of advantage-actor-critic (A2C) agents in Pommerman, a partially observable multi-agent environment with no communication. Following the COMBAT framework, we trained an agent, namely, Navocado, that won the title of the top 1 learning agent in the NeurIPS 2018 Pommerman Competition. Two critical features of our agent are worth mentioning. Firstly, our agent did not learn from any demonstrations. Secondly, our agent is highly reproducible. As a technical report, we articulate the design of state space, action space, reward, and most importantly, the COMBAT framework for our Pommerman agent. We show in the experiments that Pommerman is a perfect environment for studying continual learning, and the agent can improve its performance by continually learning new skills without forgetting the old ones. Finally, the result in the Pommerman Competition verifies the robustness of our agent when competing with various opponents.
Tasks	Continual Learning
Published	2018-12-18
URL	http://arxiv.org/abs/1812.07297v1
PDF	http://arxiv.org/pdf/1812.07297v1.pdf
PWC	https://paperswithcode.com/paper/continual-match-based-training-in-pommerman
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Framework

Handling Cold-Start Collaborative Filtering with Reinforcement Learning


Title	Handling Cold-Start Collaborative Filtering with Reinforcement Learning
Authors	Hima Varsha Dureddy, Zachary Kaden
Abstract	A major challenge in recommender systems is handling new users, whom are also called $\textit{cold-start}$ users. In this paper, we propose a novel approach for learning an optimal series of questions with which to interview cold-start users for movie recommender systems. We propose learning interview questions using Deep Q Networks to create user profiles to make better recommendations to cold-start users. While our proposed system is trained using a movie recommender system, our Deep Q Network model should generalize across various types of recommender systems.
Tasks	Recommendation Systems
Published	2018-06-16
URL	http://arxiv.org/abs/1806.06192v1
PDF	http://arxiv.org/pdf/1806.06192v1.pdf
PWC	https://paperswithcode.com/paper/handling-cold-start-collaborative-filtering
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Framework

A Mean Field View of the Landscape of Two-Layers Neural Networks


Title	A Mean Field View of the Landscape of Two-Layers Neural Networks
Authors	Song Mei, Andrea Montanari, Phan-Minh Nguyen
Abstract	Multi-layer neural networks are among the most powerful models in machine learning, yet the fundamental reasons for this success defy mathematical understanding. Learning a neural network requires to optimize a non-convex high-dimensional objective (risk function), a problem which is usually attacked using stochastic gradient descent (SGD). Does SGD converge to a global optimum of the risk or only to a local optimum? In the first case, does this happen because local minima are absent, or because SGD somehow avoids them? In the second, why do local minima reached by SGD have good generalization properties? In this paper we consider a simple case, namely two-layers neural networks, and prove that -in a suitable scaling limit- SGD dynamics is captured by a certain non-linear partial differential equation (PDE) that we call distributional dynamics (DD). We then consider several specific examples, and show how DD can be used to prove convergence of SGD to networks with nearly ideal generalization error. This description allows to ‘average-out’ some of the complexities of the landscape of neural networks, and can be used to prove a general convergence result for noisy SGD.
Tasks
Published	2018-04-18
URL	http://arxiv.org/abs/1804.06561v2
PDF	http://arxiv.org/pdf/1804.06561v2.pdf
PWC	https://paperswithcode.com/paper/a-mean-field-view-of-the-landscape-of-two
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Framework

Multiview Cross-supervision for Semantic Segmentation


Title	Multiview Cross-supervision for Semantic Segmentation
Authors	Yuan Yao, Hyun Soo Park
Abstract	This paper presents a semi-supervised learning framework for a customized semantic segmentation task using multiview image streams. A key challenge of the customized task lies in the limited accessibility of the labeled data due to the requirement of prohibitive manual annotation effort. We hypothesize that it is possible to leverage multiview image streams that are linked through the underlying 3D geometry, which can provide an additional supervisionary signal to train a segmentation model. We formulate a new cross-supervision method using a shape belief transfer—the segmentation belief in one image is used to predict that of the other image through epipolar geometry analogous to shape-from-silhouette. The shape belief transfer provides the upper and lower bounds of the segmentation for the unlabeled data where its gap approaches asymptotically to zero as the number of the labeled views increases. We integrate this theory to design a novel network that is agnostic to camera calibration, network model, and semantic category and bypasses the intermediate process of suboptimal 3D reconstruction. We validate this network by recognizing a customized semantic category per pixel from realworld visual data including non-human species and a subject of interest in social videos where attaining large-scale annotation data is infeasible.
Tasks	3D Reconstruction, Calibration, Semantic Segmentation
Published	2018-12-04
URL	http://arxiv.org/abs/1812.01738v1
PDF	http://arxiv.org/pdf/1812.01738v1.pdf
PWC	https://paperswithcode.com/paper/multiview-cross-supervision-for-semantic
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