January 27, 2020

3210 words 16 mins read

Paper Group ANR 1330

Unsupervised Data Uncertainty Learning in Visual Retrieval Systems. Shape Detection of Liver From 2D Ultrasound Images. Two-Stage Convolutional Neural Network Architecture for Lung Nodule Detection. Discrete Optimal Graph Clustering. Distribution Calibration for Regression. Computer-aided Detection of Squamous Carcinoma of the Cervix in Whole Slide …

Unsupervised Data Uncertainty Learning in Visual Retrieval Systems


Title	Unsupervised Data Uncertainty Learning in Visual Retrieval Systems
Authors	Ahmed Taha, Yi-Ting Chen, Teruhisa Misu, Abhinav Shrivastava, Larry Davis
Abstract	We introduce an unsupervised formulation to estimate heteroscedastic uncertainty in retrieval systems. We propose an extension to triplet loss that models data uncertainty for each input. Besides improving performance, our formulation models local noise in the embedding space. It quantifies input uncertainty and thus enhances interpretability of the system. This helps identify noisy observations in query and search databases. Evaluation on both image and video retrieval applications highlight the utility of our approach. We highlight our efficiency in modeling local noise using two real-world datasets: Clothing1M and Honda Driving datasets. Qualitative results illustrate our ability in identifying confusing scenarios in various domains. Uncertainty learning also enables data cleaning by detecting noisy training labels.
Tasks	Video Retrieval
Published	2019-02-07
URL	http://arxiv.org/abs/1902.02586v1
PDF	http://arxiv.org/pdf/1902.02586v1.pdf
PWC	https://paperswithcode.com/paper/unsupervised-data-uncertainty-learning-in
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Shape Detection of Liver From 2D Ultrasound Images


Title	Shape Detection of Liver From 2D Ultrasound Images
Authors	Md Abdul Mutalab Shaykat, Yashna Islam, Mohammad Ishtiaque Hossain
Abstract	Applications of ultrasound images have expanded from fetal imaging to abdominal and cardiac diagnosis. Liver-being the largest gland in the body and responsible for metabolic activities requires to be to be diagnosed and therefore subject to utmost injury. Although, ultrasound imaging has developed into three and four dimensions providing higher amount of information; it requires highly trained medical staff due to the image complexity and dimensions it contain. Since 2D ultrasound images are still considered to be the basis of clinical treatments,computer aided automated liver diagnosis is very essential. Due to the limitations of ultrasound images, such as loss of resolution leading to speckle noise, it is difficult to detect shape of organs.In this project, we propose a shape detection method for liver in 2D Ultrasound images. Then we compare the accuracies of the method for both noise and after noise removal.
Tasks
Published	2019-11-23
URL	https://arxiv.org/abs/1911.10352v1
PDF	https://arxiv.org/pdf/1911.10352v1.pdf
PWC	https://paperswithcode.com/paper/shape-detection-of-liver-from-2d-ultrasound
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Two-Stage Convolutional Neural Network Architecture for Lung Nodule Detection


Title	Two-Stage Convolutional Neural Network Architecture for Lung Nodule Detection
Authors	Haichao Cao, Hong Liu, Enmin Song, Guangzhi Ma, Xiangyang Xu, Renchao Jin, Tengying Liu, Chih-Cheng Hung
Abstract	Early detection of lung cancer is an effective way to improve the survival rate of patients. It is a critical step to have accurate detection of lung nodules in computed tomography (CT) images for the diagnosis of lung cancer. However, due to the heterogeneity of the lung nodules and the complexity of the surrounding environment, robust nodule detection has been a challenging task. In this study, we propose a two-stage convolutional neural network (TSCNN) architecture for lung nodule detection. The CNN architecture in the first stage is based on the improved UNet segmentation network to establish an initial detection of lung nodules. Simultaneously, in order to obtain a high recall rate without introducing excessive false positive nodules, we propose a novel sampling strategy, and use the offline hard mining idea for training and prediction according to the proposed cascaded prediction method. The CNN architecture in the second stage is based on the proposed dual pooling structure, which is built into three 3D CNN classification networks for false positive reduction. Since the network training requires a significant amount of training data, we adopt a data augmentation method based on random mask. Furthermore, we have improved the generalization ability of the false positive reduction model by means of ensemble learning. The proposed method has been experimentally verified on the LUNA dataset. Experimental results show that the proposed TSCNN architecture can obtain competitive detection performance.
Tasks	Computed Tomography (CT), Data Augmentation, Lung Nodule Detection
Published	2019-05-09
URL	https://arxiv.org/abs/1905.03445v1
PDF	https://arxiv.org/pdf/1905.03445v1.pdf
PWC	https://paperswithcode.com/paper/190503445
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Discrete Optimal Graph Clustering


Title	Discrete Optimal Graph Clustering
Authors	Yudong Han, Lei Zhu, Zhiyong Cheng, Jingjing Li, Xiaobai Liu
Abstract	Graph based clustering is one of the major clustering methods. Most of it work in three separate steps: similarity graph construction, clustering label relaxing and label discretization with k-means. Such common practice has three disadvantages: 1) the predefined similarity graph is often fixed and may not be optimal for the subsequent clustering. 2) the relaxing process of cluster labels may cause significant information loss. 3) label discretization may deviate from the real clustering result since k-means is sensitive to the initialization of cluster centroids. To tackle these problems, in this paper, we propose an effective discrete optimal graph clustering (DOGC) framework. A structured similarity graph that is theoretically optimal for clustering performance is adaptively learned with a guidance of reasonable rank constraint. Besides, to avoid the information loss, we explicitly enforce a discrete transformation on the intermediate continuous label, which derives a tractable optimization problem with discrete solution. Further, to compensate the unreliability of the learned labels and enhance the clustering accuracy, we design an adaptive robust module that learns prediction function for the unseen data based on the learned discrete cluster labels. Finally, an iterative optimization strategy guaranteed with convergence is developed to directly solve the clustering results. Extensive experiments conducted on both real and synthetic datasets demonstrate the superiority of our proposed methods compared with several state-of-the-art clustering approaches.
Tasks	Graph Clustering, graph construction
Published	2019-04-25
URL	http://arxiv.org/abs/1904.11266v1
PDF	http://arxiv.org/pdf/1904.11266v1.pdf
PWC	https://paperswithcode.com/paper/discrete-optimal-graph-clustering
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Distribution Calibration for Regression


Title	Distribution Calibration for Regression
Authors	Hao Song, Tom Diethe, Meelis Kull, Peter Flach
Abstract	We are concerned with obtaining well-calibrated output distributions from regression models. Such distributions allow us to quantify the uncertainty that the model has regarding the predicted target value. We introduce the novel concept of distribution calibration, and demonstrate its advantages over the existing definition of quantile calibration. We further propose a post-hoc approach to improving the predictions from previously trained regression models, using multi-output Gaussian Processes with a novel Beta link function. The proposed method is experimentally verified on a set of common regression models and shows improvements for both distribution-level and quantile-level calibration.
Tasks	Calibration, Gaussian Processes
Published	2019-05-15
URL	https://arxiv.org/abs/1905.06023v1
PDF	https://arxiv.org/pdf/1905.06023v1.pdf
PWC	https://paperswithcode.com/paper/distribution-calibration-for-regression
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Computer-aided Detection of Squamous Carcinoma of the Cervix in Whole Slide Images


Title	Computer-aided Detection of Squamous Carcinoma of the Cervix in Whole Slide Images
Authors	Ye Tian, Li Yang, Wei Wang, Jing Zhang, Qing Tang, Mili Ji, Yang Yu, Yu Li, Hong Yang, Airong Qian
Abstract	Goal: Squamous cell carcinoma of cervix is one of the most prevalent cancer worldwide in females. Traditionally, the most indispensable diagnosis of cervix squamous carcinoma is histopathological assessment which is achieved under microscope by pathologist. However, human evaluation of pathology slide is highly depending on the experience of pathologist, thus big inter- and intra-observer variability exists. Digital pathology, in combination with deep learning provides an opportunity to improve the objectivity and efficiency of histopathologic slide analysis. Methods: In this study, we obtained 800 haematoxylin and eosin stained slides from 300 patients suffered from cervix squamous carcinoma. Based on information from morphological heterogeneity in the tumor and its adjacent area, we established deep learning models using popular convolution neural network architectures (inception-v3, InceptionResnet-v2 and Resnet50). Then random forest was introduced to feature extractions and slide-based classification. Results: The overall performance of our proposed models on slide-based tumor discrimination were outstanding with an AUC scores > 0.94. While, location identifications of lesions in whole slide images were mediocre (FROC scores > 0.52) duo to the extreme complexity of tumor tissues. Conclusion: For the first time, our analysis workflow highlighted a quantitative visual-based slide analysis of cervix squamous carcinoma. Significance: This study demonstrates a pathway to assist pathologist and accelerate the diagnosis of patients by utilizing new computational approaches.
Tasks
Published	2019-05-27
URL	https://arxiv.org/abs/1905.10959v1
PDF	https://arxiv.org/pdf/1905.10959v1.pdf
PWC	https://paperswithcode.com/paper/computer-aided-detection-of-squamous
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Recommendation from Raw Data with Adaptive Compound Poisson Factorization


Title	Recommendation from Raw Data with Adaptive Compound Poisson Factorization
Authors	Olivier Gouvert, Thomas Oberlin, Cédric Févotte
Abstract	Count data are often used in recommender systems: they are widespread (song play counts, product purchases, clicks on web pages) and can reveal user preference without any explicit rating from the user. Such data are known to be sparse, over-dispersed and bursty, which makes their direct use in recommender systems challenging, often leading to pre-processing steps such as binarization. The aim of this paper is to build recommender systems from these raw data, by means of the recently proposed compound Poisson Factorization (cPF). The paper contributions are three-fold: we present a unified framework for discrete data (dcPF), leading to an adaptive and scalable algorithm; we show that our framework achieves a trade-off between Poisson Factorization (PF) applied to raw and binarized data; we study four specific instances that are relevant to recommendation and exhibit new links with combinatorics. Experiments with three different datasets show that dcPF is able to effectively adjust to over-dispersion, leading to better recommendation scores when compared with PF on either raw or binarized data.
Tasks	Recommendation Systems
Published	2019-05-20
URL	https://arxiv.org/abs/1905.13128v2
PDF	https://arxiv.org/pdf/1905.13128v2.pdf
PWC	https://paperswithcode.com/paper/190513128
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A 3D Probabilistic Deep Learning System for Detection and Diagnosis of Lung Cancer Using Low-Dose CT Scans


Title	A 3D Probabilistic Deep Learning System for Detection and Diagnosis of Lung Cancer Using Low-Dose CT Scans
Authors	Onur Ozdemir, Rebecca L. Russell, Andrew A. Berlin
Abstract	We introduce a new computer aided detection and diagnosis system for lung cancer screening with low-dose CT scans that produces meaningful probability assessments. Our system is based entirely on 3D convolutional neural networks and achieves state-of-the-art performance for both lung nodule detection and malignancy classification tasks on the publicly available LUNA16 and Kaggle Data Science Bowl challenges. While nodule detection systems are typically designed and optimized on their own, we find that it is important to consider the coupling between detection and diagnosis components. Exploiting this coupling allows us to develop an end-to-end system that has higher and more robust performance and eliminates the need for a nodule detection false positive reduction stage. Furthermore, we characterize model uncertainty in our deep learning systems, a first for lung CT analysis, and show that we can use this to provide well-calibrated classification probabilities for both nodule detection and patient malignancy diagnosis. These calibrated probabilities informed by model uncertainty can be used for subsequent risk-based decision making towards diagnostic interventions or disease treatments, as we demonstrate using a probability-based patient referral strategy to further improve our results.
Tasks	Decision Making, Lung Nodule Detection
Published	2019-02-08
URL	https://arxiv.org/abs/1902.03233v3
PDF	https://arxiv.org/pdf/1902.03233v3.pdf
PWC	https://paperswithcode.com/paper/a-3d-probabilistic-deep-learning-system-for
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ColorMapGAN: Unsupervised Domain Adaptation for Semantic Segmentation Using Color Mapping Generative Adversarial Networks


Title	ColorMapGAN: Unsupervised Domain Adaptation for Semantic Segmentation Using Color Mapping Generative Adversarial Networks
Authors	Onur Tasar, S L Happy, Yuliya Tarabalka, Pierre Alliez
Abstract	Due to the various reasons such as atmospheric effects and differences in acquisition, it is often the case that there exists a large difference between spectral bands of satellite images collected from different geographic locations. The large shift between spectral distributions of training and test data causes the current state of the art supervised learning approaches to output unsatisfactory maps. We present a novel semantic segmentation framework that is robust to such shift. The key component of the proposed framework is Color Mapping Generative Adversarial Networks (ColorMapGAN), which can generate fake training images that are semantically exactly the same as training images, but whose spectral distribution is similar to the distribution of the test images. We then use the fake images and the ground-truth for the training images to fine-tune the already trained classifier. Contrary to the existing Generative Adversarial Networks (GANs), the generator in ColorMapGAN does not have any convolutional or pooling layers. It learns to transform the colors of the training data to the colors of the test data by performing only one element-wise matrix multiplication and one matrix addition operations. Thanks to the architecturally simple but powerful design of ColorMapGAN, the proposed framework outperforms the existing approaches with a large margin in terms of both accuracy and computational complexity.
Tasks	Domain Adaptation, Semantic Segmentation, Unsupervised Domain Adaptation
Published	2019-07-30
URL	https://arxiv.org/abs/1907.12859v2
PDF	https://arxiv.org/pdf/1907.12859v2.pdf
PWC	https://paperswithcode.com/paper/colormapgan-unsupervised-domain-adaptation
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Predicting popularity of EV charging infrastructure from GIS data


Title	Predicting popularity of EV charging infrastructure from GIS data
Authors	Milan Straka, Pasquale De Falco, Gabriella Ferruzzi, Daniela Proto, Gijs van der Poel, Shahab Khormali, Ľuboš Buzna
Abstract	The availability of charging infrastructure is essential for large-scale adoption of electric vehicles (EV). Charging patterns and the utilization of infrastructure have consequences not only for the energy demand, loading local power grids but influence the economic returns, parking policies and further adoption of EVs. We develop a data-driven approach that is exploiting predictors compiled from GIS data describing the urban context and urban activities near charging infrastructure to explore correlations with a comprehensive set of indicators measuring the performance of charging infrastructure. The best fit was identified for the size of the unique group of visitors (popularity) attracted by the charging infrastructure. Consecutively, charging infrastructure is ranked by popularity. The question of whether or not a given charging spot belongs to the top tier is posed as a binary classification problem and predictive performance of logistic regression regularized with an l-1 penalty, random forests and gradient boosted regression trees is evaluated. Obtained results indicate that the collected predictors contain information that can be used to predict the popularity of charging infrastructure. The significance of predictors and how they are linked with the popularity are explored as well. The proposed methodology can be used to inform charging infrastructure deployment strategies.
Tasks
Published	2019-10-06
URL	https://arxiv.org/abs/1910.02498v1
PDF	https://arxiv.org/pdf/1910.02498v1.pdf
PWC	https://paperswithcode.com/paper/predicting-popularity-of-ev-charging
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Imitation Learning for Non-Autoregressive Neural Machine Translation


Title	Imitation Learning for Non-Autoregressive Neural Machine Translation
Authors	Bingzhen Wei, Mingxuan Wang, Hao Zhou, Junyang Lin, Jun Xie, Xu Sun
Abstract	Non-autoregressive translation models (NAT) have achieved impressive inference speedup. A potential issue of the existing NAT algorithms, however, is that the decoding is conducted in parallel, without directly considering previous context. In this paper, we propose an imitation learning framework for non-autoregressive machine translation, which still enjoys the fast translation speed but gives comparable translation performance compared to its auto-regressive counterpart. We conduct experiments on the IWSLT16, WMT14 and WMT16 datasets. Our proposed model achieves a significant speedup over the autoregressive models, while keeping the translation quality comparable to the autoregressive models. By sampling sentence length in parallel at inference time, we achieve the performance of 31.85 BLEU on WMT16 Ro$\rightarrow$En and 30.68 BLEU on IWSLT16 En$\rightarrow$De.
Tasks	Imitation Learning, Machine Translation
Published	2019-06-05
URL	https://arxiv.org/abs/1906.02041v2
PDF	https://arxiv.org/pdf/1906.02041v2.pdf
PWC	https://paperswithcode.com/paper/imitation-learning-for-non-autoregressive
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DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compression


Title	DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compression
Authors	Hanlin Tang, Xiangru Lian, Chen Yu, Tong Zhang, Ji Liu
Abstract	A standard approach in large scale machine learning is distributed stochastic gradient training, which requires the computation of aggregated stochastic gradients over multiple nodes on a network. Communication is a major bottleneck in such applications, and in recent years, compressed stochastic gradient methods such as QSGD (quantized SGD) and sparse SGD have been proposed to reduce communication. It was also shown that error compensation can be combined with compression to achieve better convergence in a scheme that each node compresses its local stochastic gradient and broadcast the result to all other nodes over the network in a single pass. However, such a single pass broadcast approach is not realistic in many practical implementations. For example, under the popular parameter server model for distributed learning, the worker nodes need to send the compressed local gradients to the parameter server, which performs the aggregation. The parameter server has to compress the aggregated stochastic gradient again before sending it back to the worker nodes. In this work, we provide a detailed analysis on this two-pass communication model and its asynchronous parallel variant, with error-compensated compression both on the worker nodes and on the parameter server. We show that the error-compensated stochastic gradient algorithm admits three very nice properties: 1) it is compatible with an \emph{arbitrary} compression technique; 2) it admits an improved convergence rate than the non error-compensated stochastic gradient methods such as QSGD and sparse SGD; 3) it admits linear speedup with respect to the number of workers. The empirical study is also conducted to validate our theoretical results.
Tasks
Published	2019-05-15
URL	https://arxiv.org/abs/1905.05957v3
PDF	https://arxiv.org/pdf/1905.05957v3.pdf
PWC	https://paperswithcode.com/paper/doublesqueeze-parallel-stochastic-gradient
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Gaussian Mixture Marginal Distributions for Modelling Remaining Pipe Wall Thickness of Critical Water Mains in Non-Destructive Evaluation


Title	Gaussian Mixture Marginal Distributions for Modelling Remaining Pipe Wall Thickness of Critical Water Mains in Non-Destructive Evaluation
Authors	Linh Nguyen, Jaime Valls Miro, Lei Shi, Teresa Vidal-Calleja
Abstract	Rapidly estimating the remaining wall thickness (RWT) is paramount for the non-destructive condition assessment evaluation of large critical metallic pipelines. A robotic vehicle with embedded magnetism-based sensors has been developed to traverse the inside of a pipeline and conduct inspections at the location of a break. However its sensing speed is constrained by the magnetic principle of operation, thus slowing down the overall operation in seeking dense RWT mapping. To ameliorate this drawback, this work proposes the partial scanning of the pipe and then employing Gaussian Processes (GPs) to infer RWT at the unseen pipe sections. Since GP prediction assumes to have normally distributed input data - which does correspond with real RWT measurements - Gaussian mixture (GM) models are proven in this work as fitting marginal distributions to effectively capture the probability of any RWT value in the inspected data. The effectiveness of the proposed approach is extensively validated from real-world data collected in collaboration with a water utility from a cast iron water main pipeline in Sydney, Australia.
Tasks	Gaussian Processes
Published	2019-07-02
URL	https://arxiv.org/abs/1907.01184v1
PDF	https://arxiv.org/pdf/1907.01184v1.pdf
PWC	https://paperswithcode.com/paper/gaussian-mixture-marginal-distributions-for
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Evolving Order and Chaos: Comparing Particle Swarm Optimization and Genetic Algorithms for Global Coordination of Cellular Automata


Title	Evolving Order and Chaos: Comparing Particle Swarm Optimization and Genetic Algorithms for Global Coordination of Cellular Automata
Authors	Anthony D. Rhodes
Abstract	We apply two evolutionary search algorithms: Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) to the design of Cellular Automata (CA) that can perform computational tasks requiring global coordination. In particular, we compare search efficiency for PSO and GAs applied to both the density classification problem and to the novel generation of ‘chaotic’ CA. Our work furthermore introduces a new variant of PSO, the Binary Global-Local PSO (BGL-PSO).
Tasks
Published	2019-09-08
URL	https://arxiv.org/abs/1909.03560v1
PDF	https://arxiv.org/pdf/1909.03560v1.pdf
PWC	https://paperswithcode.com/paper/evolving-order-and-chaos-comparing-particle
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Fully Convolutional Networks for Text Classification


Title	Fully Convolutional Networks for Text Classification
Authors	Jacob Anderson
Abstract	In this work I propose a new way of using fully convolutional networks for classification while allowing for input of any size. I additionally propose two modifications on the idea of attention and the benefits and detriments of using the modifications. Finally, I show suboptimal results on the ITAmoji 2018 tweet to emoji task and provide a discussion about why that might be the case as well as a proposed fix to further improve results.
Tasks	Text Classification
Published	2019-02-14
URL	http://arxiv.org/abs/1902.05575v1
PDF	http://arxiv.org/pdf/1902.05575v1.pdf
PWC	https://paperswithcode.com/paper/fully-convolutional-networks-for-text
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