pwc-archive/papers-with-abstracts huggingface dataset:
Reduction of Surgical Risk Through the Evaluation of Medical Imaging Diagnostics
Authors: Marco A. V. M. Grinet, Nuno M. Garcia, Ana I. R. Gouveia, Jose A. F. Moutinho, Abel J. P. Gomes
Date: 2020-03-08 00:00:00
Tasks: BIG-bench Machine Learning, Diagnostic

Computer aided diagnosis (CAD) of Breast Cancer (BRCA) images has been an active area of research in recent years. The main goals of this research is to develop reliable automatic methods for detecting and diagnosing different types of BRCA from diagnostic images. In this paper, we present a review of the state of the art CAD methods applied to magnetic resonance (MRI) and mammography images of BRCA patients. The review aims to provide an extensive introduction to different features extracted from BRCA images through texture and statistical analysis and to categorize deep learning frameworks and data structures capable of using metadata to aggregate relevant information to assist oncologists and radiologists. We divide the existing literature according to the imaging modality and into radiomics, machine learning, or combination of both. We also emphasize the difference between each modality and methods strengths and weaknesses and analyze their performance in detecting BRCA through a quantitative comparison. We compare the results of various approaches for implementing CAD systems for the detection of BRCA. Each approachs standard workflow components are reviewed and summary tables provided. We present an extensive literature review of radiomics feature extraction techniques and machine learning methods applied in BRCA diagnosis and detection, focusing on data preparation, data structures, pre processing and post processing strategies available in the literature. There is a growing interest on radiomic feature extraction and machine learning methods for BRCA detection through histopathological images, MRI and mammography images. However, there isnt a CAD method able to combine distinct data types to provide the best diagnostic results. Employing data fusion techniques to medical images and patient data could lead to improved detection and classification results.
Adversarial Camouflage: Hiding Physical-World Attacks with Natural Styles
Authors: Ranjie Duan, Xingjun Ma, Yisen Wang, James Bailey, A. K. Qin, Yun Yang
Date: 2020-03-08 00:00:00
Proceeding: CVPR 2020 6

Deep neural networks (DNNs) are known to be vulnerable to adversarial examples. Existing works have mostly focused on either digital adversarial examples created via small and imperceptible perturbations, or physical-world adversarial examples created with large and less realistic distortions that are easily identified by human observers. In this paper, we propose a novel approach, called Adversarial Camouflage (\emph{AdvCam}), to craft and camouflage physical-world adversarial examples into natural styles that appear legitimate to human observers. Specifically, \emph{AdvCam} transfers large adversarial perturbations into customized styles, which are then "hidden" on-target object or off-target background. Experimental evaluation shows that, in both digital and physical-world scenarios, adversarial examples crafted by \emph{AdvCam} are well camouflaged and highly stealthy, while remaining effective in fooling state-of-the-art DNN image classifiers. Hence, \emph{AdvCam} is a flexible approach that can help craft stealthy attacks to evaluate the robustness of DNNs. \emph{AdvCam} can also be used to protect private information from being detected by deep learning systems.
Toward Enabling a Reliable Quality Monitoring System for Additive Manufacturing Process using Deep Convolutional Neural Networks
Authors: Yaser Banadaki, Nariman Razaviarab, Hadi Fekrmandi, Safura Sharifi
Date: 2020-03-06 00:00:00
Tasks: Specificity

Additive Manufacturing (AM) is a crucial component of the smart industry. In this paper, we propose an automated quality grading system for the AM process using a deep convolutional neural network (CNN) model. The CNN model is trained offline using the images of the internal and surface defects in the layer-by-layer deposition of materials and tested online by studying the performance of detecting and classifying the failure in AM process at different extruder speeds and temperatures. The model demonstrates the accuracy of 94% and specificity of 96%, as well as above 75% in three classifier measures of the Fscore, the sensitivity, and precision for classifying the quality of the printing process in five grades in real-time. The proposed online model adds an automated, consistent, and non-contact quality control signal to the AM process that eliminates the manual inspection of parts after they are entirely built. The quality monitoring signal can also be used by the machine to suggest remedial actions by adjusting the parameters in real-time. The proposed quality predictive model serves as a proof-of-concept for any type of AM machines to produce reliable parts with fewer quality hiccups while limiting the waste of both time and materials.
Longevity Associated Geometry Identified in Satellite Images: Sidewalks, Driveways and Hiking Trails
Authors: Joshua J. Levy, Rebecca M. Lebeaux, Anne G. Hoen, Brock C. Christensen, Louis J. Vaickus, Todd A. MacKenzie
Date: 2020-03-05 00:00:00

Importance: Following a century of increase, life expectancy in the United States has stagnated and begun to decline in recent decades. Using satellite images and street view images prior work has demonstrated associations of the built environment with income, education, access to care and health factors such as obesity. However, assessment of learned image feature relationships with variation in crude mortality rate across the United States has been lacking. Objective: Investigate prediction of county-level mortality rates in the U.S. using satellite images. Design: Satellite images were extracted with the Google Static Maps application programming interface for 430 counties representing approximately 68.9% of the US population. A convolutional neural network was trained using crude mortality rates for each county in 2015 to predict mortality. Learned image features were interpreted using Shapley Additive Feature Explanations, clustered, and compared to mortality and its associated covariate predictors. Main Outcomes and Measures: County mortality was predicted using satellite images. Results: Predicted mortality from satellite images in a held-out test set of counties was strongly correlated to the true crude mortality rate (Pearson r=0.72). Learned image features were clustered, and we identified 10 clusters that were associated with education, income, geographical region, race and age. Conclusion and Relevance: The application of deep learning techniques to remotely-sensed features of the built environment can serve as a useful predictor of mortality in the United States. Tools that are able to identify image features associated with health-related outcomes can inform targeted public health interventions.
Unique Class Group Based Multi-Label Balancing Optimizer for Action Unit Detection
Authors: Ines Rieger, Jaspar Pahl, Dominik Seuss
Date: 2020-03-05 00:00:00
Tasks: Action Unit Detection, Gesture Recognition

Balancing methods for single-label data cannot be applied to multi-label problems as they would also resample the samples with high occurrences. We propose to reformulate this problem as an optimization problem in order to balance multi-label data. We apply this balancing algorithm to training datasets for detecting isolated facial movements, so-called Action Units. Several Action Units can describe combined emotions or physical states such as pain. As datasets in this area are limited and mostly imbalanced, we show how optimized balancing and then augmentation can improve Action Unit detection. At the IEEE Conference on Face and Gesture Recognition 2020, we ranked third in the Affective Behavior Analysis in-the-wild (ABAW) challenge for the Action Unit detection task.
Hierarchical Modes Exploring in Generative Adversarial Networks
Authors: Mengxiao Hu, Jinlong Li, Maolin Hu, Tao Hu
Date: 2020-03-05 00:00:00
Tasks: Diversity, Image Generation, Text to Image Generation, Text-to-Image Generation, Translation

In conditional Generative Adversarial Networks (cGANs), when two different initial noises are concatenated with the same conditional information, the distance between their outputs is relatively smaller, which makes minor modes likely to collapse into large modes. To prevent this happen, we proposed a hierarchical mode exploring method to alleviate mode collapse in cGANs by introducing a diversity measurement into the objective function as the regularization term. We also introduced the Expected Ratios of Expansion (ERE) into the regularization term, by minimizing the sum of differences between the real change of distance and ERE, we can control the diversity of generated images w.r.t specific-level features. We validated the proposed algorithm on four conditional image synthesis tasks including categorical generation, paired and un-paired image translation and text-to-image generation. Both qualitative and quantitative results show that the proposed method is effective in alleviating the mode collapse problem in cGANs, and can control the diversity of output images w.r.t specific-level features.
FineHand: Learning Hand Shapes for American Sign Language Recognition
Authors: Al Amin Hosain, Panneer Selvam Santhalingam, Parth Pathak, Huzefa Rangwala, Jana Kosecka
Date: 2020-03-04 00:00:00
Tasks: Gesture Recognition, Sign Language Recognition

American Sign Language recognition is a difficult gesture recognition problem, characterized by fast, highly articulate gestures. These are comprised of arm movements with different hand shapes, facial expression and head movements. Among these components, hand shape is the vital, often the most discriminative part of a gesture. In this work, we present an approach for effective learning of hand shape embeddings, which are discriminative for ASL gestures. For hand shape recognition our method uses a mix of manually labelled hand shapes and high confidence predictions to train deep convolutional neural network (CNN). The sequential gesture component is captured by recursive neural network (RNN) trained on the embeddings learned in the first stage. We will demonstrate that higher quality hand shape models can significantly improve the accuracy of final video gesture classification in challenging conditions with variety of speakers, different illumination and significant motion blurr. We compare our model to alternative approaches exploiting different modalities and representations of the data and show improved video gesture recognition accuracy on GMU-ASL51 benchmark dataset
SAM: The Sensitivity of Attribution Methods to Hyperparameters
Authors: Naman Bansal, Chirag Agarwal, Anh Nguyen
Date: 2020-03-04 00:00:00
Proceeding: CVPR 2020 6
Tasks: Sensitivity

Attribution methods can provide powerful insights into the reasons for a classifier's decision. We argue that a key desideratum of an explanation method is its robustness to input hyperparameters which are often randomly set or empirically tuned. High sensitivity to arbitrary hyperparameter choices does not only impede reproducibility but also questions the correctness of an explanation and impairs the trust of end-users. In this paper, we provide a thorough empirical study on the sensitivity of existing attribution methods. We found an alarming trend that many methods are highly sensitive to changes in their common hyperparameters e.g. even changing a random seed can yield a different explanation! Interestingly, such sensitivity is not reflected in the average explanation accuracy scores over the dataset as commonly reported in the literature. In addition, explanations generated for robust classifiers (i.e. which are trained to be invariant to pixel-wise perturbations) are surprisingly more robust than those generated for regular classifiers.
Adaptive binarization based on fuzzy integrals
Authors: Francesco Bardozzo, Borja De La Osa, Lubomira Horanska, Javier Fumanal-Idocin, Mattia delli Priscoli, Luigi Troiano, Roberto Tagliaferri, Javier Fernandez, Humberto Bustince
Date: 2020-03-04 00:00:00
Tasks: Binarization

Adaptive binarization methodologies threshold the intensity of the pixels with respect to adjacent pixels exploiting the integral images. In turn, the integral images are generally computed optimally using the summed-area-table algorithm (SAT). This document presents a new adaptive binarization technique based on fuzzy integral images through an efficient design of a modified SAT for fuzzy integrals. We define this new methodology as FLAT (Fuzzy Local Adaptive Thresholding). The experimental results show that the proposed methodology have produced an image quality thresholding often better than traditional algorithms and saliency neural networks. We propose a new generalization of the Sugeno and CF 1,2 integrals to improve existing results with an efficient integral image computation. Therefore, these new generalized fuzzy integrals can be used as a tool for grayscale processing in real-time and deep-learning applications. Index Terms: Image Thresholding, Image Processing, Fuzzy Integrals, Aggregation Functions
Deep Neural Network Perception Models and Robust Autonomous Driving Systems
Authors: Mohammad Javad Shafiee, Ahmadreza Jeddi, Amir Nazemi, Paul Fieguth, Alexander Wong
Date: 2020-03-04 00:00:00
Tasks: Autonomous Driving, Deep Learning

This paper analyzes the robustness of deep learning models in autonomous driving applications and discusses the practical solutions to address that.
ElixirNet: Relation-aware Network Architecture Adaptation for Medical Lesion Detection
Authors: Chenhan Jiang, Shaoju Wang, Hang Xu, Xiaodan Liang, Nong Xiao
Date: 2020-03-03 00:00:00
Tasks: Lesion Detection, medical image detection, Relation

Most advances in medical lesion detection network are limited to subtle modification on the conventional detection network designed for natural images. However, there exists a vast domain gap between medical images and natural images where the medical image detection often suffers from several domain-specific challenges, such as high lesion/background similarity, dominant tiny lesions, and severe class imbalance. Is a hand-crafted detection network tailored for natural image undoubtedly good enough over a discrepant medical lesion domain? Is there more powerful operations, filters, and sub-networks that better fit the medical lesion detection problem to be discovered? In this paper, we introduce a novel ElixirNet that includes three components: 1) TruncatedRPN balances positive and negative data for false positive reduction; 2) Auto-lesion Block is automatically customized for medical images to incorporate relation-aware operations among region proposals, and leads to more suitable and efficient classification and localization. 3) Relation transfer module incorporates the semantic relationship and transfers the relevant contextual information with an interpretable the graph thus alleviates the problem of lack of annotations for all types of lesions. Experiments on DeepLesion and Kits19 prove the effectiveness of ElixirNet, achieving improvement of both sensitivity and precision over FPN with fewer parameters.
Facial Expression Phoenix (FePh): An Annotated Sequenced Dataset for Facial and Emotion-Specified Expressions in Sign Language
Authors: Marie Alaghband, Niloofar Yousefi, Ivan Garibay
Date: 2020-03-03 00:00:00
Tasks: Gesture Recognition, Sign Language Recognition

Facial expressions are important parts of both gesture and sign language recognition systems. Despite the recent advances in both fields, annotated facial expression datasets in the context of sign language are still scarce resources. In this manuscript, we introduce an annotated sequenced facial expression dataset in the context of sign language, comprising over $3000$ facial images extracted from the daily news and weather forecast of the public tv-station PHOENIX. Unlike the majority of currently existing facial expression datasets, FePh provides sequenced semi-blurry facial images with different head poses, orientations, and movements. In addition, in the majority of images, identities are mouthing the words, which makes the data more challenging. To annotate this dataset we consider primary, secondary, and tertiary dyads of seven basic emotions of "sad", "surprise", "fear", "angry", "neutral", "disgust", and "happy". We also considered the "None" class if the image's facial expression could not be described by any of the aforementioned emotions. Although we provide FePh as a facial expression dataset of signers in sign language, it has a wider application in gesture recognition and Human Computer Interaction (HCI) systems.
Dense Crowds Detection and Surveillance with Drones using Density Maps
Authors: Javier Gonzalez-Trejo, Diego Mercado-Ravell
Date: 2020-03-03 00:00:00

Detecting and Counting people in a human crowd from a moving drone present challenging problems that arisefrom the constant changing in the image perspective andcamera angle. In this paper, we test two different state-of-the-art approaches, density map generation with VGG19 trainedwith the Bayes loss function and detect-then-count with FasterRCNN with ResNet50-FPN as backbone, in order to comparetheir precision for counting and detecting people in differentreal scenarios taken from a drone flight. We show empiricallythat both proposed methodologies perform especially well fordetecting and counting people in sparse crowds when thedrone is near the ground. Nevertheless, VGG19 provides betterprecision on both tasks while also being lighter than FasterRCNN. Furthermore, VGG19 outperforms Faster RCNN whendealing with dense crowds, proving to be more robust toscale variations and strong occlusions, being more suitable forsurveillance applications using drones
A Question-Centric Model for Visual Question Answering in Medical Imaging
Authors: Minh H. Vu, Tommy Löfstedt, Tufve Nyholm, Raphael Sznitman
Date: 2020-03-02 00:00:00
Tasks: Medical Image Analysis, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Deep learning methods have proven extremely effective at performing a variety of medical image analysis tasks. With their potential use in clinical routine, their lack of transparency has however been one of their few weak points, raising concerns regarding their behavior and failure modes. While most research to infer model behavior has focused on indirect strategies that estimate prediction uncertainties and visualize model support in the input image space, the ability to explicitly query a prediction model regarding its image content offers a more direct way to determine the behavior of trained models. To this end, we present a novel Visual Question Answering approach that allows an image to be queried by means of a written question. Experiments on a variety of medical and natural image datasets show that by fusing image and question features in a novel way, the proposed approach achieves an equal or higher accuracy compared to current methods.
Shape retrieval of non-rigid 3d human models
Authors: David Pickup, Xianfang Sun, Paul L. Rosin, Ralph R. Martin, Z Cheng, Zhouhui Lian, Masaki Aono, A. Ben Hamza, A Bronstein, M Bronstein, S Bu, Umberto Castellani, S Cheng, Valeria Garro, Andrea Giachetti, Afzal Godil, Luca Isaia, J. Han, Henry Johan, L Lai, Bo Li, C. Li, Haisheng Li, Roee Litman, X. Liu, Z Liu, Yijuan Lu, L. Sun, G Tam, Atsushi Tatsuma, J. Ye
Date: 2020-03-01 00:00:00
Tasks: 3D Shape Classification, 3D Shape Retrieval, Retrieval

3D models of humans are commonly used within computer graphics and vision, and so the ability to distinguish between body shapes is an important shape retrieval problem. We extend our recent paper which provided a benchmark for testing non-rigid 3D shape retrieval algorithms on 3D human models. This benchmark provided a far stricter challenge than previous shape benchmarks. We have added 145 new models for use as a separate training set, in order to standardise the training data used and provide a fairer comparison. We have also included experiments with the FAUST dataset of human scans. All participants of the previous benchmark study have taken part in the new tests reported here, many providing updated results using the new data. In addition, further participants have also taken part, and we provide extra analysis of the retrieval results. A total of 25 different shape retrieval methods.
A Review of Computational Approaches for Evaluation of Rehabilitation Exercises
Authors: Yalin Liao, Aleksandar Vakanski, Min Xian, David Paul, Russell Baker
Date: 2020-02-29 00:00:00
Tasks: BIG-bench Machine Learning, Feature Engineering

Recent advances in data analytics and computer-aided diagnostics stimulate the vision of patient-centric precision healthcare, where treatment plans are customized based on the health records and needs of every patient. In physical rehabilitation, the progress in machine learning and the advent of affordable and reliable motion capture sensors have been conducive to the development of approaches for automated assessment of patient performance and progress toward functional recovery. The presented study reviews computational approaches for evaluating patient performance in rehabilitation programs using motion capture systems. Such approaches will play an important role in supplementing traditional rehabilitation assessment performed by trained clinicians, and in assisting patients participating in home-based rehabilitation. The reviewed computational methods for exercise evaluation are grouped into three main categories: discrete movement score, rule-based, and template-based approaches. The review places an emphasis on the application of machine learning methods for movement evaluation in rehabilitation. Related work in the literature on data representation, feature engineering, movement segmentation, and scoring functions is presented. The study also reviews existing sensors for capturing rehabilitation movements and provides an informative listing of pertinent benchmark datasets. The significance of this paper is in being the first to provide a comprehensive review of computational methods for evaluation of patient performance in rehabilitation programs.
"Who is Driving around Me?" Unique Vehicle Instance Classification using Deep Neural Features
Authors: Tim Oosterhuis, Lambert Schomaker
Date: 2020-02-29 00:00:00
Tasks: General Classification, Object, object-detection, Object Detection, Self-Driving Cars

Being aware of other traffic is a prerequisite for self-driving cars to operate in the real world. In this paper, we show how the intrinsic feature maps of an object detection CNN can be used to uniquely identify vehicles from a dash-cam feed. Feature maps of a pretrained `YOLO' network are used to create 700 deep integrated feature signatures (DIFS) from 20 different images of 35 vehicles from a high resolution dataset and 340 signatures from 20 different images of 17 vehicles of a lower resolution tracking benchmark dataset. The YOLO network was trained to classify general object categories, e.g. classify a detected object as a `car' or `truck'. 5-Fold nearest neighbor (1NN) classification was used on DIFS created from feature maps in the middle layers of the network to correctly identify unique vehicles at a rate of 96.7\% for the high resolution data and with a rate of 86.8\% for the lower resolution data. We conclude that a deep neural detection network trained to distinguish between different classes can be successfully used to identify different instances belonging to the same class, through the creation of deep integrated feature signatures (DIFS).