pwc-archive/papers-with-abstracts huggingface dataset:
Direct Automatic Coronary Calcium Scoring in Cardiac and Chest CT
Authors: Bob D. de Vos, Jelmer M. Wolterink, Tim Leiner, Pim A. de Jong, Nikolas Lessmann, Ivana Isgum
Date: 2019-02-12 00:00:00

Cardiovascular disease (CVD) is the global leading cause of death. A strong
risk factor for CVD events is the amount of coronary artery calcium (CAC). To
meet demands of the increasing interest in quantification of CAC, i.e. coronary
calcium scoring, especially as an unrequested finding for screening and
research, automatic methods have been proposed. Current automatic calcium
scoring methods are relatively computationally expensive and only provide
scores for one type of CT. To address this, we propose a computationally
efficient method that employs two ConvNets: the first performs registration to
align the fields of view of input CTs and the second performs direct regression
of the calcium score, thereby circumventing time-consuming intermediate CAC
segmentation. Optional decision feedback provides insight in the regions that
contributed to the calcium score. Experiments were performed using 903 cardiac
CT and 1,687 chest CT scans. The method predicted calcium scores in less than
0.3 s. Intra-class correlation coefficient between predicted and manual calcium
scores was 0.98 for both cardiac and chest CT. The method showed almost perfect
agreement between automatic and manual CVD risk categorization in both
datasets, with a linearly weighted Cohen's kappa of 0.95 in cardiac CT and 0.93
in chest CT. Performance is similar to that of state-of-the-art methods, but
the proposed method is hundreds of times faster. By providing visual feedback,
insight is given in the decision process, making it readily implementable in
clinical and research settings.
Machine Learning of Time Series Using Time-delay Embedding and Precision Annealing
Authors: Alexander J. A. Ty, Zheng Fang, Rivver A. Gonzalez, Paul J. Rozdeba, Henry D. I. Abarbanel
Date: 2019-02-12 00:00:00
Tasks: BIG-bench Machine Learning, Time Series, Time Series Analysis

Tasking machine learning to predict segments of a time series requires estimating the parameters of a ML model with input/output pairs from the time series. Using the equivalence between statistical data assimilation and supervised machine learning, we revisit this task. The training method for the machine utilizes a precision annealing approach to identifying the global minimum of the action (-log[P]). In this way we are able to identify the number of training pairs required to produce good generalizations (predictions) for the time series. We proceed from a scalar time series $s(t_n); t_n = t_0 + n \Delta t$ and using methods of nonlinear time series analysis show how to produce a $D_E > 1$ dimensional time delay embedding space in which the time series has no false neighbors as does the observed $s(t_n)$ time series. In that $D_E$-dimensional space we explore the use of feed forward multi-layer perceptrons as network models operating on $D_E$-dimensional input and producing $D_E$-dimensional outputs.
Deep learning algorithms out-perform veterinary pathologists in detecting the mitotically most active tumor region
Authors: Marc Aubreville, Christof A. Bertram, Christian Marzahl, Corinne Gurtner, Martina Dettwiler, Anja Schmidt, Florian Bartenschlager, Sophie Merz, Marco Fragoso, Olivia Kershaw, Robert Klopfleisch, Andreas Maier
Date: 2019-02-12 00:00:00
Tasks: object-detection, Object Detection, whole slide images

Manual count of mitotic figures, which is determined in the tumor region with the highest mitotic activity, is a key parameter of most tumor grading schemes. It can be, however, strongly dependent on the area selection due to uneven mitotic figure distribution in the tumor section.We aimed to assess the question, how significantly the area selection could impact the mitotic count, which has a known high inter-rater disagreement. On a data set of 32 whole slide images of H&E-stained canine cutaneous mast cell tumor, fully annotated for mitotic figures, we asked eight veterinary pathologists (five board-certified, three in training) to select a field of interest for the mitotic count. To assess the potential difference on the mitotic count, we compared the mitotic count of the selected regions to the overall distribution on the slide.Additionally, we evaluated three deep learning-based methods for the assessment of highest mitotic density: In one approach, the model would directly try to predict the mitotic count for the presented image patches as a regression task. The second method aims at deriving a segmentation mask for mitotic figures, which is then used to obtain a mitotic density. Finally, we evaluated a two-stage object-detection pipeline based on state-of-the-art architectures to identify individual mitotic figures. We found that the predictions by all models were, on average, better than those of the experts. The two-stage object detector performed best and outperformed most of the human pathologists on the majority of tumor cases. The correlation between the predicted and the ground truth mitotic count was also best for this approach (0.963 to 0.979). Further, we found considerable differences in position selection between pathologists, which could partially explain the high variance that has been reported for the manual mitotic count.
Improving Facial Emotion Recognition Systems Using Gradient and Laplacian Images
Authors: Ram Krishna Pandey, Souvik Karmakar, A. G. Ramakrishnan, Nabagata Saha
Date: 2019-02-12 00:00:00
Tasks: Emotion Recognition, Facial Emotion Recognition

In this work, we have proposed several enhancements to improve the
performance of any facial emotion recognition (FER) system. We believe that the
changes in the positions of the fiducial points and the intensities capture the
crucial information regarding the emotion of a face image. We propose the use
of the gradient and the Laplacian of the input image together with the original
input into a convolutional neural network (CNN). These modifications help the
network learn additional information from the gradient and Laplacian of the
images. However, the plain CNN is not able to extract this information from the
raw images. We have performed a number of experiments on two well known
datasets KDEF and FERplus. Our approach enhances the already high performance
of state-of-the-art FER systems by 3 to 5%.
Handcrafted and Deep Trackers: Recent Visual Object Tracking Approaches and Trends
Authors: Mustansar Fiaz, Arif Mahmood, Sajid Javed, Soon Ki Jung
Date: 2018-12-06 00:00:00
Tasks: Autonomous Vehicles, Object Tracking, Visual Object Tracking

In recent years visual object tracking has become a very active research
area. An increasing number of tracking algorithms are being proposed each year.
It is because tracking has wide applications in various real world problems
such as human-computer interaction, autonomous vehicles, robotics, surveillance
and security just to name a few. In the current study, we review latest trends
and advances in the tracking area and evaluate the robustness of different
trackers based on the feature extraction methods. The first part of this work
comprises a comprehensive survey of the recently proposed trackers. We broadly
categorize trackers into Correlation Filter based Trackers (CFTs) and Non-CFTs.
Each category is further classified into various types based on the
architecture and the tracking mechanism. In the second part, we experimentally
evaluated 24 recent trackers for robustness, and compared handcrafted and deep
feature based trackers. We observe that trackers using deep features performed
better, though in some cases a fusion of both increased performance
significantly. In order to overcome the drawbacks of the existing benchmarks, a
new benchmark Object Tracking and Temple Color (OTTC) has also been proposed
and used in the evaluation of different algorithms. We analyze the performance
of trackers over eleven different challenges in OTTC, and three other
benchmarks. Our study concludes that Discriminative Correlation Filter (DCF)
based trackers perform better than the others. Our study also reveals that
inclusion of different types of regularizations over DCF often results in
boosted tracking performance. Finally, we sum up our study by pointing out some
insights and indicating future trends in visual object tracking field.
Yelp Food Identification via Image Feature Extraction and Classification
Authors: Fanbo Sun, Zhixiang Gu, Bo Feng
Date: 2019-02-11 00:00:00
Tasks: Classification, General Classification, Image Augmentation

Yelp has been one of the most popular local service search engine in US since
2004. It is powered by crowd-sourced text reviews and photo reviews. Restaurant
customers and business owners upload photo images to Yelp, including reviewing
or advertising either food, drinks, or inside and outside decorations. It is
obviously not so effective that labels for food photos rely on human editors,
which is an issue should be addressed by innovative machine learning
approaches. In this paper, we present a simple but effective approach which can
identify up to ten kinds of food via raw photos from the challenge dataset. We
use 1) image pre-processing techniques, including filtering and image
augmentation, 2) feature extraction via convolutional neural networks (CNN),
and 3) three ways of classification algorithms. Then, we illustrate the
classification accuracy by tuning parameters for augmentations, CNN, and
classification. Our experimental results show this simple but effective
approach to identify up to 10 food types from images.
Probabilistic Generative Deep Learning for Molecular Design
Authors: Daniel T. Chang
Date: 2019-02-11 00:00:00
Tasks: Deep Learning

Probabilistic generative deep learning for molecular design involves the
discovery and design of new molecules and analysis of their structure,
properties and activities by probabilistic generative models using the deep
learning approach. It leverages the existing huge databases and publications of
experimental results, and quantum-mechanical calculations, to learn and explore
molecular structure, properties and activities. We discuss the major components
of probabilistic generative deep learning for molecular design, which include
molecular structure, molecular representations, deep generative models,
molecular latent representations and latent space, molecular structure-property
and structure-activity relationships, molecular similarity and molecular
design. We highlight significant recent work using or applicable to this new
approach.
Semi-Supervised and Task-Driven Data Augmentation
Authors: Krishna Chaitanya, Neerav Karani, Christian Baumgartner, Olivio Donati, Anton Becker, Ender Konukoglu
Date: 2019-02-11 00:00:00
Tasks: Data Augmentation, Segmentation

Supervised deep learning methods for segmentation require large amounts of
labelled training data, without which they are prone to overfitting, not
generalizing well to unseen images. In practice, obtaining a large number of
annotations from clinical experts is expensive and time-consuming. One way to
address scarcity of annotated examples is data augmentation using random
spatial and intensity transformations. Recently, it has been proposed to use
generative models to synthesize realistic training examples, complementing the
random augmentation. So far, these methods have yielded limited gains over the
random augmentation. However, there is potential to improve the approach by (i)
explicitly modeling deformation fields (non-affine spatial transformation) and
intensity transformations and (ii) leveraging unlabelled data during the
generative process. With this motivation, we propose a novel task-driven data
augmentation method where to synthesize new training examples, a generative
network explicitly models and applies deformation fields and additive intensity
masks on existing labelled data, modeling shape and intensity variations,
respectively. Crucially, the generative model is optimized to be conducive to
the task, in this case segmentation, and constrained to match the distribution
of images observed from labelled and unlabelled samples. Furthermore, explicit
modeling of deformation fields allow synthesizing segmentation masks and images
in exact correspondence by simply applying the generated transformation to an
input image and the corresponding annotation. Our experiments on cardiac
magnetic resonance images (MRI) showed that, for the task of segmentation in
small training data scenarios, the proposed method substantially outperforms
conventional augmentation techniques.
A Tangent Distance Preserving Dimensionality Reduction Algorithm
Authors: Xu Zhao, Zongli Jiang
Date: 2019-02-04 00:00:00
Tasks: Dimensionality Reduction

This paper considers the problem of nonlinear dimensionality reduction.
Unlike existing methods, such as LLE, ISOMAP, which attempt to unfold the true
manifold in the low dimensional space, our algorithm tries to preserve the
nonlinear structure of the manifold, and shows how the manifold is folded in
the high dimensional space. We call this method Tangent Distance Preserving
Mapping (TDPM). TDPM uses tangent distance instead of geodesic distance, and
then applies MDS to the tangent distance matrix to map the manifold into a low
dimensional space in which we can get its nonlinear structure.
Robust Encoder-Decoder Learning Framework towards Offline Handwritten Mathematical Expression Recognition Based on Multi-Scale Deep Neural Network
Authors: Guangcun Shan, Hongyu Wang, Wei Liang
Date: 2019-02-08 00:00:00
Tasks: Decoder

Offline handwritten mathematical expression recognition is a challenging task, because handwritten mathematical expressions mainly have two problems in the process of recognition. On one hand, it is how to correctly recognize different mathematical symbols. On the other hand, it is how to correctly recognize the two-dimensional structure existing in mathematical expressions. Inspired by recent work in deep learning, a new neural network model that combines a Multi-Scale convolutional neural network (CNN) with an Attention recurrent neural network (RNN) is proposed to identify two-dimensional handwritten mathematical expressions as one-dimensional LaTeX sequences. As a result, the model proposed in the present work has achieved a WER error of 25.715% and ExpRate of 28.216%.
UrbanFM: Inferring Fine-Grained Urban Flows
Authors: Yuxuan Liang, Kun Ouyang, Lin Jing, Sijie Ruan, Ye Liu, Junbo Zhang, David S. Rosenblum, Yu Zheng
Date: 2019-02-06 00:00:00
Tasks: Fine-Grained Urban Flow Inference

Urban flow monitoring systems play important roles in smart city efforts
around the world. However, the ubiquitous deployment of monitoring devices,
such as CCTVs, induces a long-lasting and enormous cost for maintenance and
operation. This suggests the need for a technology that can reduce the number
of deployed devices, while preventing the degeneration of data accuracy and
granularity. In this paper, we aim to infer the real-time and fine-grained
crowd flows throughout a city based on coarse-grained observations. This task
is challenging due to two reasons: the spatial correlations between coarse- and
fine-grained urban flows, and the complexities of external impacts. To tackle
these issues, we develop a method entitled UrbanFM based on deep neural
networks. Our model consists of two major parts: 1) an inference network to
generate fine-grained flow distributions from coarse-grained inputs by using a
feature extraction module and a novel distributional upsampling module; 2) a
general fusion subnet to further boost the performance by considering the
influences of different external factors. Extensive experiments on two
real-world datasets, namely TaxiBJ and HappyValley, validate the effectiveness
and efficiency of our method compared to seven baselines, demonstrating the
state-of-the-art performance of our approach on the fine-grained urban flow
inference problem.
Learning icons appearance similarity
Authors: Manuel Lagunas, Elena Garces, Diego Gutierrez
Date: 2019-02-01 00:00:00
Tasks: Navigate

Selecting an optimal set of icons is a crucial step in the pipeline of visual
design to structure and navigate through content. However, designing the icons
sets is usually a difficult task for which expert knowledge is required. In
this work, to ease the process of icon set selection to the users, we propose a
similarity metric which captures the properties of style and visual identity.
We train a Siamese Neural Network with an online dataset of icons organized in
visually coherent collections that are used to adaptively sample training data
and optimize the training process. As the dataset contains noise, we further
collect human-rated information on the perception of icon's similarity which
will be used for evaluating and testing the proposed model. We present several
results and applications based on searches, kernel visualizations and optimized
set proposals that can be helpful for designers and non-expert users while
exploring large collections of icons.
Improving Dense Crowd Counting Convolutional Neural Networks using Inverse k-Nearest Neighbor Maps and Multiscale Upsampling
Authors: Greg Olmschenk, Hao Tang, Zhigang Zhu
Date: 2019-01-31 00:00:00
Tasks: Crowd Counting, Management

Gatherings of thousands to millions of people frequently occur for an
enormous variety of events, and automated counting of these high-density crowds
is useful for safety, management, and measuring significance of an event. In
this work, we show that the regularly accepted labeling scheme of crowd density
maps for training deep neural networks is less effective than our alternative
inverse k-nearest neighbor (i$k$NN) maps, even when used directly in existing
state-of-the-art network structures. We also provide a new network architecture
MUD-i$k$NN, which uses multi-scale upsampling via transposed convolutions to
take full advantage of the provided i$k$NN labeling. This upsampling combined
with the i$k$NN maps further improves crowd counting accuracy. Our new network
architecture performs favorably in comparison with the state-of-the-art.
However, our labeling and upsampling techniques are generally applicable to
existing crowd counting architectures.
Understanding Beauty via Deep Facial Features
Authors: Xudong Liu, Tao Li, Hao Peng, Iris Chuoying Ouyang, Taehwan Kim, Ruizhe Wang
Date: 2019-01-30 00:00:00
Tasks: Generative Adversarial Network

The concept of beauty has been debated by philosophers and psychologists for
centuries, but most definitions are subjective and metaphysical, and deficit in
accuracy, generality, and scalability. In this paper, we present a novel study
on mining beauty semantics of facial attributes based on big data, with an
attempt to objectively construct descriptions of beauty in a quantitative
manner. We first deploy a deep convolutional neural network (CNN) to extract
facial attributes, and then investigate correlations between these features and
attractiveness on two large-scale datasets labelled with beauty scores. Not
only do we discover the secrets of beauty verified by statistical significance
tests, our findings also align perfectly with existing psychological studies
that, e.g., small nose, high cheekbones, and femininity contribute to
attractiveness. We further leverage these high-level representations to
original images by a generative adversarial network (GAN). Beauty enhancements
after synthesis are visually compelling and statistically convincing verified
by a user survey of 10,000 data points.
License Plate Recognition with Compressive Sensing Based Feature Extraction
Authors: Andrej Jokic, Nikola Vukovic
Date: 2019-02-07 00:00:00
Tasks: Compressive Sensing, Dimensionality Reduction, General Classification, License Plate Recognition

License plate recognition is the key component to many automatic traffic
control systems. It enables the automatic identification of vehicles in many
applications. Such systems must be able to identify vehicles from images taken
in various conditions including low light, rain, snow, etc. In order to reduce
the complexity and cost of the hardware required for such devices, the
algorithm should be as efficient as possible. This paper proposes a license
plate recognition system which uses a new approach based on compressive sensing
techniques for dimensionality reduction and feature extraction. Dimensionality
reduction will enable precise classification with less training data while
demanding less computational power. Based on the extracted features, character
recognition and classification is done by a Support Vector Machine classifier.
Simultaneous x, y Pixel Estimation and Feature Extraction for Multiple Small Objects in a Scene: A Description of the ALIEN Network
Authors: Seth Zuckerman, Timothy Klein, Alexander Boxer, Christopher Goldman, Brian Lang
Date: 2019-02-06 00:00:00
Tasks: vehicle detection

We present a deep-learning network that detects multiple small objects
(hundreds to thousands) in a scene while simultaneously estimating their x,y
pixel locations together with a characteristic feature-set (for instance,
target orientation and color). All estimations are performed in a single,
forward pass which makes implementing the network fast and efficient. In this
paper, we describe the architecture of our network --- nicknamed ALIEN --- and
detail its performance when applied to vehicle detection.
Face Recognition using Compressive Sensing
Authors: Slavko Kovacevic, Vuko Djaletic, Jelena Vukovic
Date: 2019-02-06 00:00:00
Tasks: Compressive Sensing, Face Recognition

This paper deals with the Compressive Sensing implementation in the Face
Recognition problem. Compressive Sensing is new approach in signal processing
with a single goal to recover signal from small set of available samples.
Compressive Sensing finds its usage in many real applications as it lowers the
memory demand and acquisition time, and therefore allows dealing with huge data
in the fastest manner. In this paper, the undersampled signal is recovered
using the algorithm based on Total Variation minimization. The theory is
verified with an experimental results using different percentage of signal
samples.
Fingerprint Recognition under Missing Image Pixels Scenario
Authors: Dejan Brajovic, Kristina Tomovic, Jovan Radonjic
Date: 2019-02-06 00:00:00
Tasks: Compressive Sensing, Image Reconstruction, Person Identification

This work observed the problem of fingerprint image recognition in the case
of missing pixels from the original image. The possibility of missing pixels
recovery is tested by applying the Compressive Sensing approach. Namely,
different percentage of missing pixels is observed and the image reconstruction
is done by applying commonly used approach for sparse image reconstruction. The
theory is verified by experiments, showing successful image reconstruction and
later person identification even if less then 90% of the image pixels is
missing.