pwc-archive/papers-with-abstracts huggingface dataset:
Generative Adversarial U-Net for Domain-free Medical Image Augmentation
Authors: Xiaocong Chen, Yun Li, Lina Yao, Ehsan Adeli, Yu Zhang
Date: 2021-01-12 00:00:00
Tasks: Computed Tomography (CT), Generative Adversarial Network, Image Augmentation, Image Manipulation

The shortage of annotated medical images is one of the biggest challenges in the field of medical image computing. Without a sufficient number of training samples, deep learning based models are very likely to suffer from over-fitting problem. The common solution is image manipulation such as image rotation, cropping, or resizing. Those methods can help relieve the over-fitting problem as more training samples are introduced. However, they do not really introduce new images with additional information and may lead to data leakage as the test set may contain similar samples which appear in the training set. To address this challenge, we propose to generate diverse images with generative adversarial network. In this paper, we develop a novel generative method named generative adversarial U-Net , which utilizes both generative adversarial network and U-Net. Different from existing approaches, our newly designed model is domain-free and generalizable to various medical images. Extensive experiments are conducted over eight diverse datasets including computed tomography (CT) scan, pathology, X-ray, etc. The visualization and quantitative results demonstrate the efficacy and good generalization of the proposed method on generating a wide array of high-quality medical images.
Embedded Computer Vision System Applied to a Four-Legged Line Follower Robot
Authors: Beatriz Arruda Asfora
Date: 2021-01-12 00:00:00
Tasks: Decision Making

Robotics can be defined as the connection of perception to action. Taking this further, this project aims to drive a robot using an automated computer vision embedded system, connecting the robot's vision to its behavior. In order to implement a color recognition system on the robot, open source tools are chosen, such as Processing language, Android system, Arduino platform and Pixy camera. The constraints are clear: simplicity, replicability and financial viability. In order to integrate Robotics, Computer Vision and Image Processing, the robot is applied on a typical mobile robot's issue: line following. The problem of distinguishing the path from the background is analyzed through different approaches: the popular Otsu's Method, thresholding based on color combinations through experimentation and color tracking via hue and saturation. Decision making of where to move next is based on the line center of the path and is fully automated. Using a four-legged robot as platform and a camera as its only sensor, the robot is capable of successfully follow a line. From capturing the image to moving the robot, it's evident how integrative Robotics can be. The issue of this paper alone involves knowledge of Mechanical Engineering, Electronics, Control Systems and Programming. Everything related to this work was documented and made available on an open source online page, so it can be useful in learning and experimenting with robotics.
Plug-and-Play Algorithms for Video Snapshot Compressive Imaging
Authors: Xin Yuan, Yang Liu, Jinli Suo, Frédo Durand, Qionghai Dai
Date: 2021-01-13 00:00:00
Tasks: Demosaicking, Denoising

We consider the reconstruction problem of video snapshot compressive imaging (SCI), which captures high-speed videos using a low-speed 2D sensor (detector). The underlying principle of SCI is to modulate sequential high-speed frames with different masks and then these encoded frames are integrated into a snapshot on the sensor and thus the sensor can be of low-speed. On one hand, video SCI enjoys the advantages of low-bandwidth, low-power and low-cost. On the other hand, applying SCI to large-scale problems (HD or UHD videos) in our daily life is still challenging and one of the bottlenecks lies in the reconstruction algorithm. Exiting algorithms are either too slow (iterative optimization algorithms) or not flexible to the encoding process (deep learning based end-to-end networks). In this paper, we develop fast and flexible algorithms for SCI based on the plug-and-play (PnP) framework. In addition to the PnP-ADMM method, we further propose the PnP-GAP (generalized alternating projection) algorithm with a lower computational workload. We first employ the image deep denoising priors to show that PnP can recover a UHD color video with 30 frames from a snapshot measurement. Since videos have strong temporal correlation, by employing the video deep denoising priors, we achieve a significant improvement in the results. Furthermore, we extend the proposed PnP algorithms to the color SCI system using mosaic sensors, where each pixel only captures the red, green or blue channels. A joint reconstruction and demosaicing paradigm is developed for flexible and high quality reconstruction of color video SCI systems. Extensive results on both simulation and real datasets verify the superiority of our proposed algorithm.
A reusable pipeline for large-scale fiber segmentation on unidirectional fiber beds using fully convolutional neural networks
Authors: Alexandre Fioravante de Siqueira, Daniela Mayumi Ushizima, Stéfan van der Walt
Date: 2021-01-13 00:00:00

Fiber-reinforced ceramic-matrix composites are advanced materials resistant to high temperatures, with application to aerospace engineering. Their analysis depends on the detection of embedded fibers, with semi-supervised techniques usually employed to separate fibers within the fiber beds. Here we present an open computational pipeline to detect fibers in ex-situ X-ray computed tomography fiber beds. To separate the fibers in these samples, we tested four different architectures of fully convolutional neural networks. When comparing our neural network approach to a semi-supervised one, we obtained Dice and Matthews coefficients greater than $92.28 \pm 9.65\%$, reaching up to $98.42 \pm 0.03 \%$, showing that the network results are close to the human-supervised ones in these fiber beds, in some cases separating fibers that human-curated algorithms could not find. The software we generated in this project is open source, released under a permissive license, and can be freely adapted and re-used in other domains. All data and instructions on how to download and use it are also available.
Robust GPS-Vision Localization via Integrity-Driven Landmark Attention
Authors: Sriramya Bhamidipati, Grace Xingxin Gao
Date: 2021-01-13 00:00:00
Tasks: Position

For robust GPS-vision navigation in urban areas, we propose an Integrity-driven Landmark Attention (ILA) technique via stochastic reachability. Inspired by cognitive attention in humans, we perform convex optimization to select a subset of landmarks from GPS and vision measurements that maximizes integrity-driven performance. Given known measurement error bounds in non-faulty conditions, our ILA follows a unified approach to address both GPS and vision faults and is compatible with any off-the-shelf estimator. We analyze measurement deviation to estimate the stochastic reachable set of expected position for each landmark, which is parameterized via probabilistic zonotope (p-Zonotope). We apply set union to formulate a p-Zonotopic cost that represents the size of position bounds based on landmark inclusion/exclusion. We jointly minimize the p-Zonotopic cost and maximize the number of landmarks via convex relaxation. For an urban dataset, we demonstrate improved localization accuracy and robust predicted availability for a pre-defined alert limit.
Learning to be EXACT, Cell Detection for Asthma on Partially Annotated Whole Slide Images
Authors: Christian Marzahl, Christof A. Bertram, Frauke Wilm, Jörn Voigt, Ann K. Barton, Robert Klopfleisch, Katharina Breininger, Andreas Maier, Marc Aubreville
Date: 2021-01-13 00:00:00
Tasks: Cell Detection, object-detection, Object Detection, whole slide images

Asthma is a chronic inflammatory disorder of the lower respiratory tract and naturally occurs in humans and animals including horses. The annotation of an asthma microscopy whole slide image (WSI) is an extremely labour-intensive task due to the hundreds of thousands of cells per WSI. To overcome the limitation of annotating WSI incompletely, we developed a training pipeline which can train a deep learning-based object detection model with partially annotated WSIs and compensate class imbalances on the fly. With this approach we can freely sample from annotated WSIs areas and are not restricted to fully annotated extracted sub-images of the WSI as with classical approaches. We evaluated our pipeline in a cross-validation setup with a fixed training set using a dataset of six equine WSIs of which four are partially annotated and used for training, and two fully annotated WSI are used for validation and testing. Our WSI-based training approach outperformed classical sub-image-based training methods by up to 15\% $mAP$ and yielded human-like performance when compared to the annotations of ten trained pathologists.
EventAnchor: Reducing Human Interactions in Event Annotation of Racket Sports Videos
Authors: Dazhen Deng, Jiang Wu, Jiachen Wang, Yihong Wu, Xiao Xie, Zheng Zhou, HUI ZHANG, Xiaolong Zhang, Yingcai Wu
Date: 2021-01-13 00:00:00

The popularity of racket sports (e.g., tennis and table tennis) leads to high demands for data analysis, such as notational analysis, on player performance. While sports videos offer many benefits for such analysis, retrieving accurate information from sports videos could be challenging. In this paper, we propose EventAnchor, a data analysis framework to facilitate interactive annotation of racket sports video with the support of computer vision algorithms. Our approach uses machine learning models in computer vision to help users acquire essential events from videos (e.g., serve, the ball bouncing on the court) and offers users a set of interactive tools for data annotation. An evaluation study on a table tennis annotation system built on this framework shows significant improvement of user performances in simple annotation tasks on objects of interest and complex annotation tasks requiring domain knowledge.
A Lumen Segmentation Method in Ureteroscopy Images based on a Deep Residual U-Net architecture
Authors: Jorge F. Lazo, Aldo Marzullo, Sara Moccia, Michele Catellani, Benoit Rosa, Michel de Mathelin, Elena De Momi
Date: 2021-01-13 00:00:00
Tasks: Navigate

Ureteroscopy is becoming the first surgical treatment option for the majority of urinary affections. This procedure is performed using an endoscope which provides the surgeon with the visual information necessary to navigate inside the urinary tract. Having in mind the development of surgical assistance systems, that could enhance the performance of surgeon, the task of lumen segmentation is a fundamental part since this is the visual reference which marks the path that the endoscope should follow. This is something that has not been analyzed in ureteroscopy data before. However, this task presents several challenges given the image quality and the conditions itself of ureteroscopy procedures. In this paper, we study the implementation of a Deep Neural Network which exploits the advantage of residual units in an architecture based on U-Net. For the training of these networks, we analyze the use of two different color spaces: gray-scale and RGB data images. We found that training on gray-scale images gives the best results obtaining mean values of Dice Score, Precision, and Recall of 0.73, 0.58, and 0.92 respectively. The results obtained shows that the use of residual U-Net could be a suitable model for further development for a computer-aided system for navigation and guidance through the urinary system.
Detection and extraction of biological particles in a three-dimensional imaging of biological structures by TEM (Transmission Electron Microscopy)
Authors: Mariam El Oussini
Date: 2020-12-14 00:00:00
Tasks: Segmentation

Cells segmentation shows rapid growth in biology. Indeed, using the classical segmentation methods only is not enough to segment this type of images. In this manuscript, we will present a new method of ribosomes segmentation. A pre-treatment phase will precedes the segmentation process and after that a post-processing will proceed.
Relatively Lazy: Indoor-Outdoor Navigation Using Vision and GNSS
Authors: Benjamin Congram, Timothy D. Barfoot
Date: 2021-01-13 00:00:00

Visual Teach and Repeat has shown relative navigation is a robust and efficient solution for autonomous vision-based path following in difficult environments. Adding additional absolute sensors such as Global Navigation Satellite Systems (GNSS) has the potential to expand the domain of Visual Teach and Repeat to environments where the ability to visually localize is not guaranteed. Our method of lazy mapping and delaying estimation until a path-tracking error is needed avoids the need to estimate absolute states. As a result, map optimization is not required and paths can be driven immediately after being taught. We validate our approach on a real robot through an experiment in a joint indoor-outdoor environment comprising 3.5km of autonomous route repeating across a variety of lighting conditions. We achieve smooth error signals throughout the runs despite large sections of dropout for each sensor.
VoxelHop: Successive Subspace Learning for ALS Disease Classification Using Structural MRI
Authors: Xiaofeng Liu, Fangxu Xing, Chao Yang, C. -C. Jay Kuo, Suma Babu, Georges El Fakhri, Thomas Jenkins, Jonghye Woo
Date: 2021-01-13 00:00:00
Tasks: Classification, Dimensionality Reduction, General Classification

Deep learning has great potential for accurate detection and classification of diseases with medical imaging data, but the performance is often limited by the number of training datasets and memory requirements. In addition, many deep learning models are considered a "black-box," thereby often limiting their adoption in clinical applications. To address this, we present a successive subspace learning model, termed VoxelHop, for accurate classification of Amyotrophic Lateral Sclerosis (ALS) using T2-weighted structural MRI data. Compared with popular convolutional neural network (CNN) architectures, VoxelHop has modular and transparent structures with fewer parameters without any backpropagation, so it is well-suited to small dataset size and 3D imaging data. Our VoxelHop has four key components, including (1) sequential expansion of near-to-far neighborhood for multi-channel 3D data; (2) subspace approximation for unsupervised dimension reduction; (3) label-assisted regression for supervised dimension reduction; and (4) concatenation of features and classification between controls and patients. Our experimental results demonstrate that our framework using a total of 20 controls and 26 patients achieves an accuracy of 93.48$\%$ and an AUC score of 0.9394 in differentiating patients from controls, even with a relatively small number of datasets, showing its robustness and effectiveness. Our thorough evaluations also show its validity and superiority to the state-of-the-art 3D CNN classification methods. Our framework can easily be generalized to other classification tasks using different imaging modalities.
Three Dimensional MR Image Synthesis with Progressive Generative Adversarial Networks
Authors: Muzaffer Özbey, Mahmut Yurt, Salman Ul Hassan Dar, Tolga Çukur
Date: 2020-12-18 00:00:00
Tasks: Image Generation

Mainstream deep models for three-dimensional MRI synthesis are either cross-sectional or volumetric depending on the input. Cross-sectional models can decrease the model complexity, but they may lead to discontinuity artifacts. On the other hand, volumetric models can alleviate the discontinuity artifacts, but they might suffer from loss of spatial resolution due to increased model complexity coupled with scarce training data. To mitigate the limitations of both approaches, we propose a novel model that progressively recovers the target volume via simpler synthesis tasks across individual orientations.
Joint aggregation of cardinal and ordinal evaluations with an application to a student paper competition
Authors: Dorit S. Hochbaum, Erick Moreno-Centeno
Date: 2021-01-12 00:00:00
Tasks: Decision Making

An important problem in decision theory concerns the aggregation of individual rankings/ratings into a collective evaluation. We illustrate a new aggregation method in the context of the 2007 MSOM's student paper competition. The aggregation problem in this competition poses two challenges. Firstly, each paper was reviewed only by a very small fraction of the judges; thus the aggregate evaluation is highly sensitive to the subjective scales chosen by the judges. Secondly, the judges provided both cardinal and ordinal evaluations (ratings and rankings) of the papers they reviewed. The contribution here is a new robust methodology that jointly aggregates ordinal and cardinal evaluations into a collective evaluation. This methodology is particularly suitable in cases of incomplete evaluations -- i.e., when the individuals evaluate only a strict subset of the objects. This approach is potentially useful in managerial decision making problems by a committee selecting projects from a large set or capital budgeting involving multiple priorities.
Scalable Anytime Planning for Multi-Agent MDPs
Authors: Shushman Choudhury, Jayesh K. Gupta, Peter Morales, Mykel J. Kochenderfer
Date: 2021-01-12 00:00:00

We present a scalable tree search planning algorithm for large multi-agent sequential decision problems that require dynamic collaboration. Teams of agents need to coordinate decisions in many domains, but naive approaches fail due to the exponential growth of the joint action space with the number of agents. We circumvent this complexity through an anytime approach that allows us to trade computation for approximation quality and also dynamically coordinate actions. Our algorithm comprises three elements: online planning with Monte Carlo Tree Search (MCTS), factored representations of local agent interactions with coordination graphs, and the iterative Max-Plus method for joint action selection. We evaluate our approach on the benchmark SysAdmin domain with static coordination graphs and achieve comparable performance with much lower computation cost than our MCTS baselines. We also introduce a multi-drone delivery domain with dynamic, i.e., state-dependent coordination graphs, and demonstrate how our approach scales to large problems on this domain that are intractable for other MCTS methods. We provide an open-source implementation of our algorithm at https://github.com/JuliaPOMDP/FactoredValueMCTS.jl.
Top Program Construction and Reduction for polynomial time Meta-Interpretive Learning
Authors: Stassa Patsantzis, Stephen H. Muggleton
Date: 2021-01-13 00:00:00

Meta-Interpretive Learners, like most ILP systems, learn by searching for a correct hypothesis in the hypothesis space, the powerset of all constructible clauses. We show how this exponentially-growing search can be replaced by the construction of a Top program: the set of clauses in all correct hypotheses that is itself a correct hypothesis. We give an algorithm for Top program construction and show that it constructs a correct Top program in polynomial time and from a finite number of examples. We implement our algorithm in Prolog as the basis of a new MIL system, Louise, that constructs a Top program and then reduces it by removing redundant clauses. We compare Louise to the state-of-the-art search-based MIL system Metagol in experiments on grid world navigation, graph connectedness and grammar learning datasets and find that Louise improves on Metagol's predictive accuracy when the hypothesis space and the target theory are both large, or when the hypothesis space does not include a correct hypothesis because of "classification noise" in the form of mislabelled examples. When the hypothesis space or the target theory are small, Louise and Metagol perform equally well.
Formalising Concepts as Grounded Abstractions
Authors: Stephen Clark, Alexander Lerchner, Tamara von Glehn, Olivier Tieleman, Richard Tanburn, Misha Dashevskiy, Matko Bosnjak
Date: 2021-01-13 00:00:00
Tasks: Representation Learning

The notion of concept has been studied for centuries, by philosophers, linguists, cognitive scientists, and researchers in artificial intelligence (Margolis & Laurence, 1999). There is a large literature on formal, mathematical models of concepts, including a whole sub-field of AI -- Formal Concept Analysis -- devoted to this topic (Ganter & Obiedkov, 2016). Recently, researchers in machine learning have begun to investigate how methods from representation learning can be used to induce concepts from raw perceptual data (Higgins, Sonnerat, et al., 2018). The goal of this report is to provide a formal account of concepts which is compatible with this latest work in deep learning. The main technical goal of this report is to show how techniques from representation learning can be married with a lattice-theoretic formulation of conceptual spaces. The mathematics of partial orders and lattices is a standard tool for modelling conceptual spaces (Ch.2, Mitchell (1997), Ganter and Obiedkov (2016)); however, there is no formal work that we are aware of which defines a conceptual lattice on top of a representation that is induced using unsupervised deep learning (Goodfellow et al., 2016). The advantages of partially-ordered lattice structures are that these provide natural mechanisms for use in concept discovery algorithms, through the meets and joins of the lattice.
Expanding Explainability: Towards Social Transparency in AI systems
Authors: Upol Ehsan, Q. Vera Liao, Michael Muller, Mark O. Riedl, Justin D. Weisz
Date: 2021-01-12 00:00:00
Tasks: Decision Making, Explainable Artificial Intelligence (XAI)

As AI-powered systems increasingly mediate consequential decision-making, their explainability is critical for end-users to take informed and accountable actions. Explanations in human-human interactions are socially-situated. AI systems are often socio-organizationally embedded. However, Explainable AI (XAI) approaches have been predominantly algorithm-centered. We take a developmental step towards socially-situated XAI by introducing and exploring Social Transparency (ST), a sociotechnically informed perspective that incorporates the socio-organizational context into explaining AI-mediated decision-making. To explore ST conceptually, we conducted interviews with 29 AI users and practitioners grounded in a speculative design scenario. We suggested constitutive design elements of ST and developed a conceptual framework to unpack ST's effect and implications at the technical, decision-making, and organizational level. The framework showcases how ST can potentially calibrate trust in AI, improve decision-making, facilitate organizational collective actions, and cultivate holistic explainability. Our work contributes to the discourse of Human-Centered XAI by expanding the design space of XAI.
Model-Based Machine Learning for Communications
Authors: Nir Shlezinger, Nariman Farsad, Yonina C. Eldar, Andrea J. Goldsmith
Date: 2021-01-12 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, model

We present an introduction to model-based machine learning for communication systems. We begin by reviewing existing strategies for combining model-based algorithms and machine learning from a high level perspective, and compare them to the conventional deep learning approach which utilizes established deep neural network (DNN) architectures trained in an end-to-end manner. Then, we focus on symbol detection, which is one of the fundamental tasks of communication receivers. We show how the different strategies of conventional deep architectures, deep unfolding, and DNN-aided hybrid algorithms, can be applied to this problem. The last two approaches constitute a middle ground between purely model-based and solely DNN-based receivers. By focusing on this specific task, we highlight the advantages and drawbacks of each strategy, and present guidelines to facilitate the design of future model-based deep learning systems for communications.