Paper Group ANR 21
RGB-Topography and X-rays Image Registration for Idiopathic Scoliosis Children Patient Follow-up. Convex Hull Monte-Carlo Tree Search. A Multiple Decoder CNN for Inverse Consistent 3D Image Registration. Medical Image Registration Using Deep Neural Networks: A Comprehensive Review. Comparative Visual Analytics for Assessing Medical Records with Seq …
RGB-Topography and X-rays Image Registration for Idiopathic Scoliosis Children Patient Follow-up
| Title | RGB-Topography and X-rays Image Registration for Idiopathic Scoliosis Children Patient Follow-up |
| Authors | Insaf Setitra, Noureddine Aouaa, Abdelkrim Meziane, Afef Benrabia, Houria Kaced, Hanene Belabassi, Sara Ait Ziane, Nadia Henda Zenati, Oualid Djekkoune |
| Abstract | Children diagnosed with a scoliosis pathology are exposed during their follow up to ionic radiations in each X-rays diagnosis. This exposure can have negative effects on the patient’s health and cause diseases in the adult age. In order to reduce X-rays scanning, recent systems provide diagnosis of scoliosis patients using solely RGB images. The output of such systems is a set of augmented images and scoliosis related angles. These angles, however, confuse the physicians due to their large number. Moreover, the lack of X-rays scans makes it impossible for the physician to compare RGB and X-rays images, and decide whether to reduce X-rays exposure or not. In this work, we exploit both RGB images of scoliosis captured during clinical diagnosis, and X-rays hard copies provided by patients in order to register both images and give a rich comparison of diagnoses. The work consists in, first, establishing the monomodal (RGB topography of the back) and multimodal (RGB and Xrays) image database, then registering images based on patient landmarks, and finally blending registered images for a visual analysis and follow up by the physician. The proposed registration is based on a rigid transformation that preserves the topology of the patient’s back. Parameters of the rigid transformation are estimated using a proposed angle minimization of Cervical vertebra 7, and Posterior Superior Iliac Spine landmarks of a source and target diagnoses. Experiments conducted on the constructed database show a better monomodal and multimodal registration using our proposed method compared to registration using an Equation System Solving based registration. |
| Tasks | Image Registration |
| Published | 2020-03-20 |
| URL | https://arxiv.org/abs/2003.09404v1 |
| https://arxiv.org/pdf/2003.09404v1.pdf | |
| PWC | https://paperswithcode.com/paper/rgb-topography-and-x-rays-image-registration |
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Convex Hull Monte-Carlo Tree Search
| Title | Convex Hull Monte-Carlo Tree Search |
| Authors | Michael Painter, Bruno Lacerda, Nick Hawes |
| Abstract | This work investigates Monte-Carlo planning for agents in stochastic environments, with multiple objectives. We propose the Convex Hull Monte-Carlo Tree-Search (CHMCTS) framework, which builds upon Trial Based Heuristic Tree Search and Convex Hull Value Iteration (CHVI), as a solution to multi-objective planning in large environments. Moreover, we consider how to pose the problem of approximating multiobjective planning solutions as a contextual multi-armed bandits problem, giving a principled motivation for how to select actions from the view of contextual regret. This leads us to the use of Contextual Zooming for action selection, yielding Zooming CHMCTS. We evaluate our algorithm using the Generalised Deep Sea Treasure environment, demonstrating that Zooming CHMCTS can achieve a sublinear contextual regret and scales better than CHVI on a given computational budget. |
| Tasks | Multi-Armed Bandits |
| Published | 2020-03-09 |
| URL | https://arxiv.org/abs/2003.04445v2 |
| https://arxiv.org/pdf/2003.04445v2.pdf | |
| PWC | https://paperswithcode.com/paper/convex-hull-monte-carlo-tree-search |
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A Multiple Decoder CNN for Inverse Consistent 3D Image Registration
| Title | A Multiple Decoder CNN for Inverse Consistent 3D Image Registration |
| Authors | Abdullah Nazib, Clinton Fookes, Olivier Salvado, Dimitri Perrin |
| Abstract | The recent application of deep learning technologies in medical image registration has exponentially decreased the registration time and gradually increased registration accuracy when compared to their traditional counterparts. Most of the learning-based registration approaches considers this task as a one directional problem. As a result, only correspondence from the moving image to the target image is considered. However, in some medical procedures bidirectional registration is required to be performed. Unlike other learning-based registration, we propose a registration framework with inverse consistency. The proposed method simultaneously learns forward transformation and backward transformation in an unsupervised manner. We perform training and testing of the method on the publicly available LPBA40 MRI dataset and demonstrate strong performance than baseline registration methods. |
| Tasks | Image Registration, Medical Image Registration |
| Published | 2020-02-15 |
| URL | https://arxiv.org/abs/2002.06468v1 |
| https://arxiv.org/pdf/2002.06468v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-multiple-decoder-cnn-for-inverse-consistent |
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Medical Image Registration Using Deep Neural Networks: A Comprehensive Review
| Title | Medical Image Registration Using Deep Neural Networks: A Comprehensive Review |
| Authors | Hamid Reza Boveiri, Raouf Khayami, Reza Javidan, Ali Reza MehdiZadeh |
| Abstract | Image-guided interventions are saving the lives of a large number of patients where the image registration problem should indeed be considered as the most complex and complicated issue to be tackled. On the other hand, the recently huge progress in the field of machine learning made by the possibility of implementing deep neural networks on the contemporary many-core GPUs opened up a promising window to challenge with many medical applications, where the registration is not an exception. In this paper, a comprehensive review on the state-of-the-art literature known as medical image registration using deep neural networks is presented. The review is systematic and encompasses all the related works previously published in the field. Key concepts, statistical analysis from different points of view, confiding challenges, novelties and main contributions, key-enabling techniques, future directions and prospective trends all are discussed and surveyed in details in this comprehensive review. This review allows a deep understanding and insight for the readers active in the field who are investigating the state-of-the-art and seeking to contribute the future literature. |
| Tasks | Image Registration, Medical Image Registration |
| Published | 2020-02-09 |
| URL | https://arxiv.org/abs/2002.03401v1 |
| https://arxiv.org/pdf/2002.03401v1.pdf | |
| PWC | https://paperswithcode.com/paper/medical-image-registration-using-deep-neural |
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Comparative Visual Analytics for Assessing Medical Records with Sequence Embedding
| Title | Comparative Visual Analytics for Assessing Medical Records with Sequence Embedding |
| Authors | Rongchen Guo, Takanori Fujiwara, Yiran Li, Kelly M. Lima, Soman Sen, Nam K. Tran, Kwan-Liu Ma |
| Abstract | Machine learning for data-driven diagnosis has been actively studied in medicine to provide better healthcare. Supporting analysis of a patient cohort similar to a patient under treatment is a key task for clinicians to make decisions with high confidence. However, such analysis is not straightforward due to the characteristics of medical records: high dimensionality, irregularity in time, and sparsity. To address this challenge, we introduce a method for similarity calculation of medical records. Our method employs event and sequence embeddings. While we use an autoencoder for the event embedding, we apply its variant with the self-attention mechanism for the sequence embedding. Moreover, in order to better handle the irregularity of data, we enhance the self-attention mechanism with consideration of different time intervals. We have developed a visual analytics system to support comparative studies of patient records. To make a comparison of sequences with different lengths easier, our system incorporates a sequence alignment method. Through its interactive interface, the user can quickly identify patients of interest and conveniently review both the temporal and multivariate aspects of the patient records. We demonstrate the effectiveness of our design and system with case studies using a real-world dataset from the neonatal intensive care unit of UC Davis. |
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| Published | 2020-02-18 |
| URL | https://arxiv.org/abs/2002.08356v2 |
| https://arxiv.org/pdf/2002.08356v2.pdf | |
| PWC | https://paperswithcode.com/paper/comparative-visual-analytics-for-assessing |
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Theory In, Theory Out: The uses of social theory in machine learning for social science
| Title | Theory In, Theory Out: The uses of social theory in machine learning for social science |
| Authors | Jason Radford, Kenneth Joseph |
| Abstract | Research at the intersection of machine learning and the social sciences has provided critical new insights into social behavior. At the same time, a variety of critiques have been raised ranging from technical issues with the data used and features constructed, problematic assumptions built into models, their limited interpretability, and their contribution to bias and inequality. We argue such issues arise primarily because of the lack of social theory at various stages of the model building and analysis. In the first half of this paper, we walk through how social theory can be used to answer the basic methodological and interpretive questions that arise at each stage of the machine learning pipeline. In the second half, we show how theory can be used to assess and compare the quality of different social learning models, including interpreting, generalizing, and assessing the fairness of models. We believe this paper can act as a guide for computer and social scientists alike to navigate the substantive questions involved in applying the tools of machine learning to social data. |
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| Published | 2020-01-09 |
| URL | https://arxiv.org/abs/2001.03203v3 |
| https://arxiv.org/pdf/2001.03203v3.pdf | |
| PWC | https://paperswithcode.com/paper/theory-in-theory-out-how-social-theory-can |
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I Know Where You Are Coming From: On the Impact of Social Media Sources on AI Model Performance
| Title | I Know Where You Are Coming From: On the Impact of Social Media Sources on AI Model Performance |
| Authors | Qi Yang, Aleksandr Farseev, Andrey Filchenkov |
| Abstract | Nowadays, social networks play a crucial role in human everyday life and no longer purely associated with spare time spending. In fact, instant communication with friends and colleagues has become an essential component of our daily interaction giving a raise of multiple new social network types emergence. By participating in such networks, individuals generate a multitude of data points that describe their activities from different perspectives and, for example, can be further used for applications such as personalized recommendation or user profiling. However, the impact of the different social media networks on machine learning model performance has not been studied comprehensively yet. Particularly, the literature on modeling multi-modal data from multiple social networks is relatively sparse, which had inspired us to take a deeper dive into the topic in this preliminary study. Specifically, in this work, we will study the performance of different machine learning models when being learned on multi-modal data from different social networks. Our initial experimental results reveal that social network choice impacts the performance and the proper selection of data source is crucial. |
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| Published | 2020-02-05 |
| URL | https://arxiv.org/abs/2002.01726v1 |
| https://arxiv.org/pdf/2002.01726v1.pdf | |
| PWC | https://paperswithcode.com/paper/i-know-where-you-are-coming-from-on-the |
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Dual-discriminator GAN: A GAN way of profile face recognition
| Title | Dual-discriminator GAN: A GAN way of profile face recognition |
| Authors | Xinyu Zhang, Yang Zhao, Hao Zhang |
| Abstract | A wealth of angle problems occur when facial recognition is performed: At present, the feature extraction network presents eigenvectors with large differences between the frontal face and profile face recognition of the same person in many cases. For this reason, the state-of-the-art facial recognition network will use multiple samples for the same target to ensure that eigenvector differences caused by angles are ignored during training. However, there is another solution available, which is to generate frontal face images with profile face images before recognition. In this paper, we proposed a method of generating frontal faces with image-to-image profile faces based on Generative Adversarial Network (GAN). |
| Tasks | Face Recognition |
| Published | 2020-03-20 |
| URL | https://arxiv.org/abs/2003.09116v1 |
| https://arxiv.org/pdf/2003.09116v1.pdf | |
| PWC | https://paperswithcode.com/paper/dual-discriminator-gan-a-gan-way-of-profile |
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Deep Reinforcement Learning with Smooth Policy
| Title | Deep Reinforcement Learning with Smooth Policy |
| Authors | Qianli Shen, Yan Li, Haoming Jiang, Zhaoran Wang, Tuo Zhao |
| Abstract | Deep neural networks have been widely adopted in modern reinforcement learning (RL) algorithms with great empirical successes in various domains. However, the large search space of training a neural network requires a significant amount of data, which makes the current RL algorithms not sample efficient. Motivated by the fact that many environments with continuous state space have smooth transitions, we propose to learn a smooth policy that behaves smoothly with respect to states. In contrast to policies parameterized by linear/reproducing kernel functions, where simple regularization techniques suffice to control smoothness, for neural network based reinforcement learning algorithms, there is no readily available solution to learn a smooth policy. In this paper, we develop a new training framework — $\textbf{S}$mooth $\textbf{R}$egularized $\textbf{R}$einforcement $\textbf{L}$earning ($\textbf{SR}^2\textbf{L}$), where the policy is trained with smoothness-inducing regularization. Such regularization effectively constrains the search space of the learning algorithms and enforces smoothness in the learned policy. We apply the proposed framework to both on-policy (TRPO) and off-policy algorithm (DDPG). Through extensive experiments, we demonstrate that our method achieves improved sample efficiency. |
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| Published | 2020-03-21 |
| URL | https://arxiv.org/abs/2003.09534v2 |
| https://arxiv.org/pdf/2003.09534v2.pdf | |
| PWC | https://paperswithcode.com/paper/deep-reinforcement-learning-with-smooth |
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Weighted Average Precision: Adversarial Example Detection in the Visual Perception of Autonomous Vehicles
| Title | Weighted Average Precision: Adversarial Example Detection in the Visual Perception of Autonomous Vehicles |
| Authors | Yilan Li, Senem Velipasalar |
| Abstract | Recent works have shown that neural networks are vulnerable to carefully crafted adversarial examples (AE). By adding small perturbations to input images, AEs are able to make the victim model predicts incorrect outputs. Several research work in adversarial machine learning started to focus on the detection of AEs in autonomous driving. However, the existing studies either use preliminary assumption on outputs of detections or ignore the tracking system in the perception pipeline. In this paper, we firstly propose a novel distance metric for practical autonomous driving object detection outputs. Then, we bridge the gap between the current AE detection research and the real-world autonomous systems by providing a temporal detection algorithm, which takes the impact of tracking system into consideration. We perform evaluation on Berkeley Deep Drive (BDD) and CityScapes datasets to show how our approach outperforms existing single-frame-mAP based AE detections by increasing 17.76% accuracy of performance. |
| Tasks | Autonomous Driving, Autonomous Vehicles, Object Detection |
| Published | 2020-01-25 |
| URL | https://arxiv.org/abs/2002.03751v1 |
| https://arxiv.org/pdf/2002.03751v1.pdf | |
| PWC | https://paperswithcode.com/paper/weighted-average-precision-adversarial |
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Unsupervised Domain Adaptation for Mobile Semantic Segmentation based on Cycle Consistency and Feature Alignment
| Title | Unsupervised Domain Adaptation for Mobile Semantic Segmentation based on Cycle Consistency and Feature Alignment |
| Authors | Marco Toldo, Umberto Michieli, Gianluca Agresti, Pietro Zanuttigh |
| Abstract | The supervised training of deep networks for semantic segmentation requires a huge amount of labeled real world data. To solve this issue, a commonly exploited workaround is to use synthetic data for training, but deep networks show a critical performance drop when analyzing data with slightly different statistical properties with respect to the training set. In this work, we propose a novel Unsupervised Domain Adaptation (UDA) strategy to address the domain shift issue between real world and synthetic representations. An adversarial model, based on the cycle consistency framework, performs the mapping between the synthetic and real domain. The data is then fed to a MobileNet-v2 architecture that performs the semantic segmentation task. An additional couple of discriminators, working at the feature level of the MobileNet-v2, allows to better align the features of the two domain distributions and to further improve the performance. Finally, the consistency of the semantic maps is exploited. After an initial supervised training on synthetic data, the whole UDA architecture is trained end-to-end considering all its components at once. Experimental results show how the proposed strategy is able to obtain impressive performance in adapting a segmentation network trained on synthetic data to real world scenarios. The usage of the lightweight MobileNet-v2 architecture allows its deployment on devices with limited computational resources as the ones employed in autonomous vehicles. |
| Tasks | Autonomous Vehicles, Domain Adaptation, Semantic Segmentation, Unsupervised Domain Adaptation |
| Published | 2020-01-14 |
| URL | https://arxiv.org/abs/2001.04692v2 |
| https://arxiv.org/pdf/2001.04692v2.pdf | |
| PWC | https://paperswithcode.com/paper/unsupervised-domain-adaptation-for-mobile |
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Decentralized Optimization of Vehicle Route Planning – A Cross-City Comparative Study
| Title | Decentralized Optimization of Vehicle Route Planning – A Cross-City Comparative Study |
| Authors | Brionna Davis, Grace Jennings, Taylor Pothast, Ilias Gerostathopoulos, Evangelos Pournaras, Raphael E. Stern |
| Abstract | New mobility concepts are at the forefront of research and innovation in smart cities. The introduction of connected and autonomous vehicles enables new possibilities in vehicle routing. Specifically, knowing the origin and destination of each agent in the network can allow for real-time routing of the vehicles to optimize network performance. However, this relies on individual vehicles being “altruistic” i.e., being willing to accept an alternative non-preferred route in order to achieve a network-level performance goal. In this work, we conduct a study to compare different levels of agent altruism and the resulting effect on the network-level traffic performance. Specifically, this study compares the effects of different underlying urban structures on the overall network performance, and investigates which characteristics of the network make it possible to realize routing improvements using a decentralized optimization router. The main finding is that, with increased vehicle altruism, it is possible to balance traffic flow among the links of the network. We show evidence that the decentralized optimization router is more effective with networks of high load while we study the influence of cities characteristics, in particular: networks with a higher number of nodes (intersections) or edges (roads) per unit area allow for more possible alternate routes, and thus higher potential to improve network performance. |
| Tasks | Autonomous Vehicles |
| Published | 2020-01-10 |
| URL | https://arxiv.org/abs/2001.03384v1 |
| https://arxiv.org/pdf/2001.03384v1.pdf | |
| PWC | https://paperswithcode.com/paper/decentralized-optimization-of-vehicle-route |
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Child Face Age-Progression via Deep Feature Aging
| Title | Child Face Age-Progression via Deep Feature Aging |
| Authors | Debayan Deb, Divyansh Aggarwal, Anil K. Jain |
| Abstract | Given a gallery of face images of missing children, state-of-the-art face recognition systems fall short in identifying a child (probe) recovered at a later age. We propose a feature aging module that can age-progress deep face features output by a face matcher. In addition, the feature aging module guides age-progression in the image space such that synthesized aged faces can be utilized to enhance longitudinal face recognition performance of any face matcher without requiring any explicit training. For time lapses larger than 10 years (the missing child is found after 10 or more years), the proposed age-progression module improves the closed-set identification accuracy of FaceNet from 16.53% to 21.44% and CosFace from 60.72% to 66.12% on a child celebrity dataset, namely ITWCC. The proposed method also outperforms state-of-the-art approaches with a rank-1 identification rate of 95.91%, compared to 94.91%, on a public aging dataset, FG-NET, and 99.58%, compared to 99.50%, on CACD-VS. These results suggest that aging face features enhances the ability to identify young children who are possible victims of child trafficking or abduction. |
| Tasks | Face Recognition |
| Published | 2020-03-17 |
| URL | https://arxiv.org/abs/2003.08788v1 |
| https://arxiv.org/pdf/2003.08788v1.pdf | |
| PWC | https://paperswithcode.com/paper/child-face-age-progression-via-deep-feature |
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xCos: An Explainable Cosine Metric for Face Verification Task
| Title | xCos: An Explainable Cosine Metric for Face Verification Task |
| Authors | Yu-Sheng Lin, Zhe-Yu Liu, Yu-An Chen, Yu-Siang Wang, Hsin-Ying Lee, Yi-Rong Chen, Ya-Liang Chang, Winston H. Hsu |
| Abstract | We study the XAI (explainable AI) on the face recognition task, particularly the face verification here. Face verification is a crucial task in recent days and it has been deployed to plenty of applications, such as access control, surveillance, and automatic personal log-on for mobile devices. With the increasing amount of data, deep convolutional neural networks can achieve very high accuracy for the face verification task. Beyond exceptional performances, deep face verification models need more interpretability so that we can trust the results they generate. In this paper, we propose a novel similarity metric, called explainable cosine ($xCos$), that comes with a learnable module that can be plugged into most of the verification models to provide meaningful explanations. With the help of $xCos$, we can see which parts of the 2 input faces are similar, where the model pays its attention to, and how the local similarities are weighted to form the output $xCos$ score. We demonstrate the effectiveness of our proposed method on LFW and various competitive benchmarks, resulting in not only providing novel and desiring model interpretability for face verification but also ensuring the accuracy as plugging into existing face recognition models. |
| Tasks | Face Recognition, Face Verification |
| Published | 2020-03-11 |
| URL | https://arxiv.org/abs/2003.05383v1 |
| https://arxiv.org/pdf/2003.05383v1.pdf | |
| PWC | https://paperswithcode.com/paper/xcos-an-explainable-cosine-metric-for-face |
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An Empirical Evaluation on Robustness and Uncertainty of Regularization Methods
| Title | An Empirical Evaluation on Robustness and Uncertainty of Regularization Methods |
| Authors | Sanghyuk Chun, Seong Joon Oh, Sangdoo Yun, Dongyoon Han, Junsuk Choe, Youngjoon Yoo |
| Abstract | Despite apparent human-level performances of deep neural networks (DNN), they behave fundamentally differently from humans. They easily change predictions when small corruptions such as blur and noise are applied on the input (lack of robustness), and they often produce confident predictions on out-of-distribution samples (improper uncertainty measure). While a number of researches have aimed to address those issues, proposed solutions are typically expensive and complicated (e.g. Bayesian inference and adversarial training). Meanwhile, many simple and cheap regularization methods have been developed to enhance the generalization of classifiers. Such regularization methods have largely been overlooked as baselines for addressing the robustness and uncertainty issues, as they are not specifically designed for that. In this paper, we provide extensive empirical evaluations on the robustness and uncertainty estimates of image classifiers (CIFAR-100 and ImageNet) trained with state-of-the-art regularization methods. Furthermore, experimental results show that certain regularization methods can serve as strong baseline methods for robustness and uncertainty estimation of DNNs. |
| Tasks | Bayesian Inference |
| Published | 2020-03-09 |
| URL | https://arxiv.org/abs/2003.03879v1 |
| https://arxiv.org/pdf/2003.03879v1.pdf | |
| PWC | https://paperswithcode.com/paper/an-empirical-evaluation-on-robustness-and |
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