Paper Group ANR 36
A New 3D Segmentation Technique for QCT Scans of the Lumbar Spine to Determine BMD and Vertebral Geometry. Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media. Multi-level Feedback Web Links Selection Problem: Learning and Optimization. TrajectoryNet: An Embedded GPS Trajectory Representation for Point-based Classifi …
A New 3D Segmentation Technique for QCT Scans of the Lumbar Spine to Determine BMD and Vertebral Geometry
| Title | A New 3D Segmentation Technique for QCT Scans of the Lumbar Spine to Determine BMD and Vertebral Geometry |
| Authors | Andre Mastmeyer, Klaus Engelke, Christina Fuchs, Willi Kalender |
| Abstract | Quantitative computed tomography (QCT) is a standard method to determine bone mineral density (BMD) in the spine. Traditionally single 8 - 10 mm thick slices have been analyzed only. Current spiral CT scanners provide true 3D acquisition schemes allowing for a more differential BMD analysis and an assessment of geometric parameters, which may improve fracture prediction. We developed a novel 3D segmentation approach that combines deformable balloons, multi seeded volume growing, and dedicated morphological operations to extract the vertebral bodies. An anatomy-oriented coordinate system attached automatically to each vertebra is used to define volumes of interest. We analyzed intra-operator precision of the segmentation procedure using abdominal scans from 10 patients (60 mAs, 120 kV, slice thickness 1mm, B40s, Siemens Sensation 16). Our new segmentation method shows excellent precision errors in the order of < 1 % for BMD and < 2 % for volume. |
| Tasks | |
| Published | 2017-05-19 |
| URL | http://arxiv.org/abs/1705.08273v1 |
| http://arxiv.org/pdf/1705.08273v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-new-3d-segmentation-technique-for-qct-scans |
| Repo | |
| Framework | |
Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media
| Title | Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media |
| Authors | Amir Hossein Yazdavar, Hussein S. Al-Olimat, Monireh Ebrahimi, Goonmeet Bajaj, Tanvi Banerjee, Krishnaprasad Thirunarayan, Jyotishman Pathak, Amit Sheth |
| Abstract | With the rise of social media, millions of people are routinely expressing their moods, feelings, and daily struggles with mental health issues on social media platforms like Twitter. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of clinical depression from tweets obtained unobtrusively. Based on the analysis of tweets crawled from users with self-reported depressive symptoms in their Twitter profiles, we demonstrate the potential for detecting clinical depression symptoms which emulate the PHQ-9 questionnaire clinicians use today. Our study uses a semi-supervised statistical model to evaluate how the duration of these symptoms and their expression on Twitter (in terms of word usage patterns and topical preferences) align with the medical findings reported via the PHQ-9. Our proactive and automatic screening tool is able to identify clinical depressive symptoms with an accuracy of 68% and precision of 72%. |
| Tasks | |
| Published | 2017-10-16 |
| URL | http://arxiv.org/abs/1710.05429v1 |
| http://arxiv.org/pdf/1710.05429v1.pdf | |
| PWC | https://paperswithcode.com/paper/semi-supervised-approach-to-monitoring |
| Repo | |
| Framework | |
Multi-level Feedback Web Links Selection Problem: Learning and Optimization
| Title | Multi-level Feedback Web Links Selection Problem: Learning and Optimization |
| Authors | Kechao Cai, Kun Chen, Longbo Huang, John C. S. Lui |
| Abstract | Selecting the right web links for a website is important because appropriate links not only can provide high attractiveness but can also increase the website’s revenue. In this work, we first show that web links have an intrinsic \emph{multi-level feedback structure}. For example, consider a $2$-level feedback web link: the $1$st level feedback provides the Click-Through Rate (CTR) and the $2$nd level feedback provides the potential revenue, which collectively produce the compound $2$-level revenue. We consider the context-free links selection problem of selecting links for a homepage so as to maximize the total compound $2$-level revenue while keeping the total $1$st level feedback above a preset threshold. We further generalize the problem to links with $n~(n\ge2)$-level feedback structure. The key challenge is that the links’ multi-level feedback structures are unobservable unless the links are selected on the homepage. To our best knowledge, we are the first to model the links selection problem as a constrained multi-armed bandit problem and design an effective links selection algorithm by learning the links’ multi-level structure with provable \emph{sub-linear} regret and violation bounds. We uncover the multi-level feedback structures of web links in two real-world datasets. We also conduct extensive experiments on the datasets to compare our proposed \textbf{LExp} algorithm with two state-of-the-art context-free bandit algorithms and show that \textbf{LExp} algorithm is the most effective in links selection while satisfying the constraint. |
| Tasks | |
| Published | 2017-09-08 |
| URL | http://arxiv.org/abs/1709.02664v1 |
| http://arxiv.org/pdf/1709.02664v1.pdf | |
| PWC | https://paperswithcode.com/paper/multi-level-feedback-web-links-selection |
| Repo | |
| Framework | |
TrajectoryNet: An Embedded GPS Trajectory Representation for Point-based Classification Using Recurrent Neural Networks
| Title | TrajectoryNet: An Embedded GPS Trajectory Representation for Point-based Classification Using Recurrent Neural Networks |
| Authors | Xiang Jiang, Erico N de Souza, Ahmad Pesaranghader, Baifan Hu, Daniel L. Silver, Stan Matwin |
| Abstract | Understanding and discovering knowledge from GPS (Global Positioning System) traces of human activities is an essential topic in mobility-based urban computing. We propose TrajectoryNet-a neural network architecture for point-based trajectory classification to infer real world human transportation modes from GPS traces. To overcome the challenge of capturing the underlying latent factors in the low-dimensional and heterogeneous feature space imposed by GPS data, we develop a novel representation that embeds the original feature space into another space that can be understood as a form of basis expansion. We also enrich the feature space via segment-based information and use Maxout activations to improve the predictive power of Recurrent Neural Networks (RNNs). We achieve over 98% classification accuracy when detecting four types of transportation modes, outperforming existing models without additional sensory data or location-based prior knowledge. |
| Tasks | |
| Published | 2017-05-07 |
| URL | http://arxiv.org/abs/1705.02636v2 |
| http://arxiv.org/pdf/1705.02636v2.pdf | |
| PWC | https://paperswithcode.com/paper/trajectorynet-an-embedded-gps-trajectory |
| Repo | |
| Framework | |
A Detail Based Method for Linear Full Reference Image Quality Prediction
| Title | A Detail Based Method for Linear Full Reference Image Quality Prediction |
| Authors | Elio D. Di Claudio, Giovanni Jacovitti |
| Abstract | In this paper, a novel Full Reference method is proposed for image quality assessment, using the combination of two separate metrics to measure the perceptually distinct impact of detail losses and of spurious details. To this purpose, the gradient of the impaired image is locally decomposed as a predicted version of the original gradient, plus a gradient residual. It is assumed that the detail attenuation identifies the detail loss, whereas the gradient residuals describe the spurious details. It turns out that the perceptual impact of detail losses is roughly linear with the loss of the positional Fisher information, while the perceptual impact of the spurious details is roughly proportional to a logarithmic measure of the signal to residual ratio. The affine combination of these two metrics forms a new index strongly correlated with the empirical Differential Mean Opinion Score (DMOS) for a significant class of image impairments, as verified for three independent popular databases. The method allowed alignment and merging of DMOS data coming from these different databases to a common DMOS scale by affine transformations. Unexpectedly, the DMOS scale setting is possible by the analysis of a single image affected by additive noise. |
| Tasks | Image Quality Assessment |
| Published | 2017-09-10 |
| URL | http://arxiv.org/abs/1709.03124v3 |
| http://arxiv.org/pdf/1709.03124v3.pdf | |
| PWC | https://paperswithcode.com/paper/a-detail-based-method-for-linear-full |
| Repo | |
| Framework | |
Vessel Segmentation and Catheter Detection in X-Ray Angiograms Using Superpixels
| Title | Vessel Segmentation and Catheter Detection in X-Ray Angiograms Using Superpixels |
| Authors | Hamid R. Fazlali, Nader Karimi, S. M. Reza Soroushmehr, Shahram Shirani, Brahmajee. K. Nallamothu, Kevin R. Ward, Shadrokh Samavi, Kayvan Najarian |
| Abstract | Coronary artery disease (CAD) is the leading causes of death around the world. One of the most common imaging methods for diagnosing this disease is X-ray angiography. Diagnosing using these images is usually challenging due to non-uniform illumination, low contrast, presence of other body tissues, presence of catheter etc. These challenges make the diagnoses task of cardiologists tougher and more prone to misdiagnosis. In this paper we propose a new automated framework for coronary arteries segmentation, catheter detection and center-line extraction in x-ray angiography images. Our proposed segmentation method is based on superpixels. In this method at first three different superpixel scales are exploited and a measure for vesselness probability of each superpixel is determined. A majority voting is used for obtaining an initial segmentation map from these three superpixel scales. This initial segmentation is refined by finding the orthogonal line on each ridge pixel of vessel region. In this framework we use our catheter detection and tracking method which detects the catheter by finding its ridge in the first frame and traces in other frames by fitting a second order polynomial on it. Also we use the image ridges for extracting the coronary arteries centerlines. We evaluated our method qualitatively and quantitatively on two different challenging datasets and compared it with one of the previous well-known coronary arteries segmentation methods. Our method could detect the catheter and reduced the false positive rate in addition to achieving better segmentation results. The evaluation results prove that our method performs better in a much shorter time. |
| Tasks | |
| Published | 2017-09-08 |
| URL | http://arxiv.org/abs/1709.02741v1 |
| http://arxiv.org/pdf/1709.02741v1.pdf | |
| PWC | https://paperswithcode.com/paper/vessel-segmentation-and-catheter-detection-in |
| Repo | |
| Framework | |
Blind Stereo Image Quality Assessment Inspired by Brain Sensory-Motor Fusion
| Title | Blind Stereo Image Quality Assessment Inspired by Brain Sensory-Motor Fusion |
| Authors | Maryam Karimi, Najmeh Soltanian, Shadrokh Samavi, Nader Karimi, S. M. Reza Soroushmehr, Kayvan Najarian |
| Abstract | The use of 3D and stereo imaging is rapidly increasing. Compression, transmission, and processing could degrade the quality of stereo images. Quality assessment of such images is different than their 2D counterparts. Metrics that represent 3D perception by human visual system (HVS) are expected to assess stereoscopic quality more accurately. In this paper, inspired by brain sensory/motor fusion process, two stereo images are fused together. Then from every fused image two synthesized images are extracted. Effects of different distortions on statistical distributions of the synthesized images are shown. Based on the observed statistical changes, features are extracted from these synthesized images. These features can reveal type and severity of distortions. Then, a stacked neural network model is proposed, which learns the extracted features and accurately evaluates the quality of stereo images. This model is tested on 3D images of popular databases. Experimental results show the superiority of this method over state of the art stereo image quality assessment approaches |
| Tasks | Image Quality Assessment |
| Published | 2017-09-03 |
| URL | http://arxiv.org/abs/1709.00725v1 |
| http://arxiv.org/pdf/1709.00725v1.pdf | |
| PWC | https://paperswithcode.com/paper/blind-stereo-image-quality-assessment |
| Repo | |
| Framework | |
Learning Abduction under Partial Observability
| Title | Learning Abduction under Partial Observability |
| Authors | Brendan Juba, Zongyi Li, Evan Miller |
| Abstract | Juba recently proposed a formulation of learning abductive reasoning from examples, in which both the relative plausibility of various explanations, as well as which explanations are valid, are learned directly from data. The main shortcoming of this formulation of the task is that it assumes access to full-information (i.e., fully specified) examples; relatedly, it offers no role for declarative background knowledge, as such knowledge is rendered redundant in the abduction task by complete information. In this work, we extend the formulation to utilize such partially specified examples, along with declarative background knowledge about the missing data. We show that it is possible to use implicitly learned rules together with the explicitly given declarative knowledge to support hypotheses in the course of abduction. We observe that when a small explanation exists, it is possible to obtain a much-improved guarantee in the challenging exception-tolerant setting. Such small, human-understandable explanations are of particular interest for potential applications of the task. |
| Tasks | |
| Published | 2017-11-13 |
| URL | http://arxiv.org/abs/1711.04438v3 |
| http://arxiv.org/pdf/1711.04438v3.pdf | |
| PWC | https://paperswithcode.com/paper/learning-abduction-under-partial |
| Repo | |
| Framework | |
Progressive Boosting for Class Imbalance
| Title | Progressive Boosting for Class Imbalance |
| Authors | Roghayeh Soleymani, Eric Granger, Giorgio Fumera |
| Abstract | Pattern recognition applications often suffer from skewed data distributions between classes, which may vary during operations w.r.t. the design data. Two-class classification systems designed using skewed data tend to recognize the majority class better than the minority class of interest. Several data-level techniques have been proposed to alleviate this issue by up-sampling minority samples or under-sampling majority samples. However, some informative samples may be neglected by random under-sampling and adding synthetic positive samples through up-sampling adds to training complexity. In this paper, a new ensemble learning algorithm called Progressive Boosting (PBoost) is proposed that progressively inserts uncorrelated groups of samples into a Boosting procedure to avoid loss of information while generating a diverse pool of classifiers. Base classifiers in this ensemble are generated from one iteration to the next, using subsets from a validation set that grows gradually in size and imbalance. Consequently, PBoost is more robust to unknown and variable levels of skew in operational data, and has lower computation complexity than Boosting ensembles in literature. In PBoost, a new loss factor is proposed to avoid bias of performance towards the negative class. Using this loss factor, the weight update of samples and classifier contribution in final predictions are set based on the ability to recognize both classes. Using the proposed loss factor instead of standard accuracy can avoid biasing performance in any Boosting ensemble. The proposed approach was validated and compared using synthetic data, videos from the FIA dataset that emulates face re-identification applications, and KEEL collection of datasets. Results show that PBoost can outperform state of the art techniques in terms of both accuracy and complexity over different levels of imbalance and overlap between classes. |
| Tasks | |
| Published | 2017-06-05 |
| URL | http://arxiv.org/abs/1706.01531v1 |
| http://arxiv.org/pdf/1706.01531v1.pdf | |
| PWC | https://paperswithcode.com/paper/progressive-boosting-for-class-imbalance |
| Repo | |
| Framework | |
Terahertz Security Image Quality Assessment by No-reference Model Observers
| Title | Terahertz Security Image Quality Assessment by No-reference Model Observers |
| Authors | Menghan Hu, Xiongkuo Min, Guangtao Zhai, Wenhan Zhu, Yucheng Zhu, Zhaodi Wang, Xiaokang Yang, Guang Tian |
| Abstract | To provide the possibility of developing objective image quality assessment (IQA) algorithms for THz security images, we constructed the THz security image database (THSID) including a total of 181 THz security images with the resolution of 127*380. The main distortion types in THz security images were first analyzed for the design of subjective evaluation criteria to acquire the mean opinion scores. Subsequently, the existing no-reference IQA algorithms, which were 5 opinion-aware approaches viz., NFERM, GMLF, DIIVINE, BRISQUE and BLIINDS2, and 8 opinion-unaware approaches viz., QAC, SISBLIM, NIQE, FISBLIM, CPBD, S3 and Fish_bb, were executed for the evaluation of the THz security image quality. The statistical results demonstrated the superiority of Fish_bb over the other testing IQA approaches for assessing the THz image quality with PLCC (SROCC) values of 0.8925 (-0.8706), and with RMSE value of 0.3993. The linear regression analysis and Bland-Altman plot further verified that the Fish__bb could substitute for the subjective IQA. Nonetheless, for the classification of THz security images, we tended to use S3 as a criterion for ranking THz security image grades because of the relatively low false positive rate in classifying bad THz image quality into acceptable category (24.69%). Interestingly, due to the specific property of THz image, the average pixel intensity gave the best performance than the above complicated IQA algorithms, with the PLCC, SROCC and RMSE of 0.9001, -0.8800 and 0.3857, respectively. This study will help the users such as researchers or security staffs to obtain the THz security images of good quality. Currently, our research group is attempting to make this research more comprehensive. |
| Tasks | Image Quality Assessment |
| Published | 2017-07-12 |
| URL | http://arxiv.org/abs/1707.03574v2 |
| http://arxiv.org/pdf/1707.03574v2.pdf | |
| PWC | https://paperswithcode.com/paper/terahertz-security-image-quality-assessment |
| Repo | |
| Framework | |
Unified Neural Architecture for Drug, Disease and Clinical Entity Recognition
| Title | Unified Neural Architecture for Drug, Disease and Clinical Entity Recognition |
| Authors | Sunil Kumar Sahu, Ashish Anand |
| Abstract | Most existing methods for biomedical entity recognition task rely on explicit feature engineering where many features either are specific to a particular task or depends on output of other existing NLP tools. Neural architectures have been shown across various domains that efforts for explicit feature design can be reduced. In this work we propose an unified framework using bi-directional long short term memory network (BLSTM) for named entity recognition (NER) tasks in biomedical and clinical domains. Three important characteristics of the framework are as follows - (1) model learns contextual as well as morphological features using two different BLSTM in hierarchy, (2) model uses first order linear conditional random field (CRF) in its output layer in cascade of BLSTM to infer label or tag sequence, (3) model does not use any domain specific features or dictionary, i.e., in another words, same set of features are used in the three NER tasks, namely, disease name recognition (Disease NER), drug name recognition (Drug NER) and clinical entity recognition (Clinical NER). We compare performance of the proposed model with existing state-of-the-art models on the standard benchmark datasets of the three tasks. We show empirically that the proposed framework outperforms all existing models. Further our analysis of CRF layer and word-embedding obtained using character based embedding show their importance. |
| Tasks | Feature Engineering, Named Entity Recognition |
| Published | 2017-08-11 |
| URL | http://arxiv.org/abs/1708.03447v1 |
| http://arxiv.org/pdf/1708.03447v1.pdf | |
| PWC | https://paperswithcode.com/paper/unified-neural-architecture-for-drug-disease |
| Repo | |
| Framework | |
A Perceptually Weighted Rank Correlation Indicator for Objective Image Quality Assessment
| Title | A Perceptually Weighted Rank Correlation Indicator for Objective Image Quality Assessment |
| Authors | Qingbo Wu, Hongliang Li, Fanman Meng, King N. Ngan |
| Abstract | In the field of objective image quality assessment (IQA), the Spearman’s $\rho$ and Kendall’s $\tau$ are two most popular rank correlation indicators, which straightforwardly assign uniform weight to all quality levels and assume each pair of images are sortable. They are successful for measuring the average accuracy of an IQA metric in ranking multiple processed images. However, two important perceptual properties are ignored by them as well. Firstly, the sorting accuracy (SA) of high quality images are usually more important than the poor quality ones in many real world applications, where only the top-ranked images would be pushed to the users. Secondly, due to the subjective uncertainty in making judgement, two perceptually similar images are usually hardly sortable, whose ranks do not contribute to the evaluation of an IQA metric. To more accurately compare different IQA algorithms, we explore a perceptually weighted rank correlation indicator in this paper, which rewards the capability of correctly ranking high quality images, and suppresses the attention towards insensitive rank mistakes. More specifically, we focus on activating `valid’ pairwise comparison towards image quality, whose difference exceeds a given sensory threshold (ST). Meanwhile, each image pair is assigned an unique weight, which is determined by both the quality level and rank deviation. By modifying the perception threshold, we can illustrate the sorting accuracy with a more sophisticated SA-ST curve, rather than a single rank correlation coefficient. The proposed indicator offers a new insight for interpreting visual perception behaviors. Furthermore, the applicability of our indicator is validated in recommending robust IQA metrics for both the degraded and enhanced image data. | |
| Tasks | Image Quality Assessment |
| Published | 2017-05-15 |
| URL | http://arxiv.org/abs/1705.05126v1 |
| http://arxiv.org/pdf/1705.05126v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-perceptually-weighted-rank-correlation |
| Repo | |
| Framework | |
Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation
| Title | Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation |
| Authors | Xin-Yi Tong, Gui-Song Xia, Fan Hu, Yanfei Zhong, Mihai Datcu, Liangpei Zhang |
| Abstract | Remote sensing (RS) image retrieval is of great significant for geological information mining. Over the past two decades, a large amount of research on this task has been carried out, which mainly focuses on the following three core issues: feature extraction, similarity metric and relevance feedback. Due to the complexity and multiformity of ground objects in high-resolution remote sensing (HRRS) images, there is still room for improvement in the current retrieval approaches. In this paper, we analyze the three core issues of RS image retrieval and provide a comprehensive review on existing methods. Furthermore, for the goal to advance the state-of-the-art in HRRS image retrieval, we focus on the feature extraction issue and delve how to use powerful deep representations to address this task. We conduct systematic investigation on evaluating correlative factors that may affect the performance of deep features. By optimizing each factor, we acquire remarkable retrieval results on publicly available HRRS datasets. Finally, we explain the experimental phenomenon in detail and draw conclusions according to our analysis. Our work can serve as a guiding role for the research of content-based RS image retrieval. |
| Tasks | Image Retrieval |
| Published | 2017-07-23 |
| URL | https://arxiv.org/abs/1707.07321v3 |
| https://arxiv.org/pdf/1707.07321v3.pdf | |
| PWC | https://paperswithcode.com/paper/exploiting-deep-features-for-remote-sensing |
| Repo | |
| Framework | |
Learning non-parametric Markov networks with mutual information
| Title | Learning non-parametric Markov networks with mutual information |
| Authors | Janne Leppä-aho, Santeri Räisänen, Xiao Yang, Teemu Roos |
| Abstract | We propose a method for learning Markov network structures for continuous data without invoking any assumptions about the distribution of the variables. The method makes use of previous work on a non-parametric estimator for mutual information which is used to create a non-parametric test for multivariate conditional independence. This independence test is then combined with an efficient constraint-based algorithm for learning the graph structure. The performance of the method is evaluated on several synthetic data sets and it is shown to learn considerably more accurate structures than competing methods when the dependencies between the variables involve non-linearities. |
| Tasks | |
| Published | 2017-08-08 |
| URL | http://arxiv.org/abs/1708.02497v1 |
| http://arxiv.org/pdf/1708.02497v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-non-parametric-markov-networks-with |
| Repo | |
| Framework | |
Transform Invariant Auto-encoder
| Title | Transform Invariant Auto-encoder |
| Authors | Tadashi Matsuo, Hiroya Fukuhara, Nobutaka Shimada |
| Abstract | The auto-encoder method is a type of dimensionality reduction method. A mapping from a vector to a descriptor that represents essential information can be automatically generated from a set of vectors without any supervising information. However, an image and its spatially shifted version are encoded into different descriptors by an existing ordinary auto-encoder because each descriptor includes a spatial subpattern and its position. To generate a descriptor representing a spatial subpattern in an image, we need to normalize its spatial position in the images prior to training an ordinary auto-encoder; however, such a normalization is generally difficult for images without obvious standard positions. We propose a transform invariant auto-encoder and an inference model of transform parameters. By the proposed method, we can separate an input into a transform invariant descriptor and transform parameters. The proposed method can be applied to various auto-encoders without requiring any special modules or labeled training samples. By applying it to shift transforms, we can achieve a shift invariant auto-encoder that can extract a typical spatial subpattern independent of its relative position in a window. In addition, we can achieve a model that can infer shift parameters required to restore the input from the typical subpattern. As an example of the proposed method, we demonstrate that a descriptor generated by a shift invariant auto-encoder can represent a typical spatial subpattern. In addition, we demonstrate the imitation of a human hand by a robot hand as an example of a regression based on spatial subpatterns. |
| Tasks | Dimensionality Reduction |
| Published | 2017-09-12 |
| URL | http://arxiv.org/abs/1709.03754v1 |
| http://arxiv.org/pdf/1709.03754v1.pdf | |
| PWC | https://paperswithcode.com/paper/transform-invariant-auto-encoder |
| Repo | |
| Framework | |
