pwc-archive/papers-with-abstracts huggingface dataset:
Fully Automatic Myocardial Segmentation of Contrast Echocardiography Sequence Using Random Forests Guided by Shape Model
Authors: Yuanwei Li, Chin Pang Ho, Matthieu Toulemonde, Navtej Chahal, Roxy Senior, Meng-Xing Tang
Date: 2018-06-19 00:00:00
Tasks: Image Registration, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Myocardial contrast echocardiography (MCE) is an imaging technique that
assesses left ventricle function and myocardial perfusion for the detection of
coronary artery diseases. Automatic MCE perfusion quantification is challenging
and requires accurate segmentation of the myocardium from noisy and
time-varying images. Random forests (RF) have been successfully applied to many
medical image segmentation tasks. However, the pixel-wise RF classifier ignores
contextual relationships between label outputs of individual pixels. RF which
only utilizes local appearance features is also susceptible to data suffering
from large intensity variations. In this paper, we demonstrate how to overcome
the above limitations of classic RF by presenting a fully automatic
segmentation pipeline for myocardial segmentation in full-cycle 2D MCE data.
Specifically, a statistical shape model is used to provide shape prior
information that guide the RF segmentation in two ways. First, a novel shape
model (SM) feature is incorporated into the RF framework to generate a more
accurate RF probability map. Second, the shape model is fitted to the RF
probability map to refine and constrain the final segmentation to plausible
myocardial shapes. We further improve the performance by introducing a bounding
box detection algorithm as a preprocessing step in the segmentation pipeline.
Our approach on 2D image is further extended to 2D+t sequence which ensures
temporal consistency in the resultant sequence segmentations. When evaluated on
clinical MCE data, our proposed method achieves notable improvement in
segmentation accuracy and outperforms other state-of-the-art methods including
the classic RF and its variants, active shape model and image registration.
Joint Neural Entity Disambiguation with Output Space Search
Authors: Hamed Shahbazi, Xiaoli Z. Fern, Reza Ghaeini, Chao Ma, Rasha Obeidat, Prasad Tadepalli
Date: 2018-06-19 00:00:00
Proceeding: COLING 2018 8
Tasks: Entity Disambiguation

In this paper, we present a novel model for entity disambiguation that
combines both local contextual information and global evidences through Limited
Discrepancy Search (LDS). Given an input document, we start from a complete
solution constructed by a local model and conduct a search in the space of
possible corrections to improve the local solution from a global view point.
Our search utilizes a heuristic function to focus more on the least confident
local decisions and a pruning function to score the global solutions based on
their local fitness and the global coherences among the predicted entities.
Experimental results on CoNLL 2003 and TAC 2010 benchmarks verify the
effectiveness of our model.
On the Learning of Deep Local Features for Robust Face Spoofing Detection
Authors: Gustavo Botelho de Souza, João Paulo Papa, Aparecido Nilceu Marana
Date: 2018-06-19 00:00:00
Tasks: Face Recognition

Biometrics emerged as a robust solution for security systems. However, given
the dissemination of biometric applications, criminals are developing
techniques to circumvent them by simulating physical or behavioral traits of
legal users (spoofing attacks). Despite face being a promising characteristic
due to its universality, acceptability and presence of cameras almost
everywhere, face recognition systems are extremely vulnerable to such frauds
since they can be easily fooled with common printed facial photographs.
State-of-the-art approaches, based on Convolutional Neural Networks (CNNs),
present good results in face spoofing detection. However, these methods do not
consider the importance of learning deep local features from each facial
region, even though it is known from face recognition that each facial region
presents different visual aspects, which can also be exploited for face
spoofing detection. In this work we propose a novel CNN architecture trained in
two steps for such task. Initially, each part of the neural network learns
features from a given facial region. Afterwards, the whole model is fine-tuned
on the whole facial images. Results show that such pre-training step allows the
CNN to learn different local spoofing cues, improving the performance and the
convergence speed of the final model, outperforming the state-of-the-art
approaches.
Myocardial Segmentation of Contrast Echocardiograms Using Random Forests Guided by Shape Model
Authors: Yuanwei Li, Chin Pang Ho, Navtej Chahal, Roxy Senior, Meng-Xing Tang
Date: 2018-06-19 00:00:00
Tasks: Segmentation

Myocardial Contrast Echocardiography (MCE) with micro-bubble contrast agent
enables myocardial perfusion quantification which is invaluable for the early
detection of coronary artery diseases. In this paper, we proposed a new
segmentation method called Shape Model guided Random Forests (SMRF) for the
analysis of MCE data. The proposed method utilizes a statistical shape model of
the myocardium to guide the Random Forest (RF) segmentation in two ways. First,
we introduce a novel Shape Model (SM) feature which captures the global
structure and shape of the myocardium to produce a more accurate RF probability
map. Second, the shape model is fitted to the RF probability map to further
refine and constrain the final segmentation to plausible myocardial shapes.
Evaluated on clinical MCE images from 15 patients, our method obtained
promising results (Dice=0.81, Jaccard=0.70, MAD=1.68 mm, HD=6.53 mm) and showed
a notable improvement in segmentation accuracy over the classic RF and its
variants.
Towards the identification of Parkinson's Disease using only T1 MR Images
Authors: Sara Soltaninejad, Irene Cheng, Anup Basu
Date: 2018-06-19 00:00:00
Tasks: Classification, Diagnostic, General Classification

Parkinson's Disease (PD) is one of the most common types of neurological
diseases caused by progressive degeneration of dopamin- ergic neurons in the
brain. Even though there is no fixed cure for this neurodegenerative disease,
earlier diagnosis followed by earlier treatment can help patients have a better
quality of life. Magnetic Resonance Imag- ing (MRI) has been one of the most
popular diagnostic tool in recent years because it avoids harmful radiations.
In this paper, we investi- gate the plausibility of using MRIs for
automatically diagnosing PD. Our proposed method has three main steps : 1)
Preprocessing, 2) Fea- ture Extraction, and 3) Classification. The FreeSurfer
library is used for the first and the second steps. For classification, three
main types of classifiers, including Logistic Regression (LR), Random Forest
(RF) and Support Vector Machine (SVM), are applied and their classification
abil- ity is compared. The Parkinsons Progression Markers Initiative (PPMI)
data set is used to evaluate the proposed method. The proposed system prove to
be promising in assisting the diagnosis of PD.
Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network
Authors: Yuanwei Li, Bishesh Khanal, Benjamin Hou, Amir Alansary, Juan J. Cerrolaza, Matthew Sinclair, Jacqueline Matthew, Chandni Gupta, Caroline Knight, Bernhard Kainz, Daniel Rueckert
Date: 2018-06-19 00:00:00
Tasks: Anatomy, Multi-Task Learning

Standard scan plane detection in fetal brain ultrasound (US) forms a crucial
step in the assessment of fetal development. In clinical settings, this is done
by manually manoeuvring a 2D probe to the desired scan plane. With the advent
of 3D US, the entire fetal brain volume containing these standard planes can be
easily acquired. However, manual standard plane identification in 3D volume is
labour-intensive and requires expert knowledge of fetal anatomy. We propose a
new Iterative Transformation Network (ITN) for the automatic detection of
standard planes in 3D volumes. ITN uses a convolutional neural network to learn
the relationship between a 2D plane image and the transformation parameters
required to move that plane towards the location/orientation of the standard
plane in the 3D volume. During inference, the current plane image is passed
iteratively to the network until it converges to the standard plane location.
We explore the effect of using different transformation representations as
regression outputs of ITN. Under a multi-task learning framework, we introduce
additional classification probability outputs to the network to act as
confidence measures for the regressed transformation parameters in order to
further improve the localisation accuracy. When evaluated on 72 US volumes of
fetal brain, our method achieves an error of 3.83mm/12.7 degrees and
3.80mm/12.6 degrees for the transventricular and transcerebellar planes
respectively and takes 0.46s per plane. Source code is publicly available at
https://github.com/yuanwei1989/plane-detection.
Contrastive Explanations with Local Foil Trees
Authors: Jasper van der Waa, Marcel Robeer, Jurriaan van Diggelen, Matthieu Brinkhuis, Mark Neerincx
Date: 2018-06-19 00:00:00
Tasks: Explainable Artificial Intelligence (XAI), General Classification, Interpretable Machine Learning

Recent advances in interpretable Machine Learning (iML) and eXplainable AI
(XAI) construct explanations based on the importance of features in
classification tasks. However, in a high-dimensional feature space this
approach may become unfeasible without restraining the set of important
features. We propose to utilize the human tendency to ask questions like "Why
this output (the fact) instead of that output (the foil)?" to reduce the number
of features to those that play a main role in the asked contrast. Our proposed
method utilizes locally trained one-versus-all decision trees to identify the
disjoint set of rules that causes the tree to classify data points as the foil
and not as the fact. In this study we illustrate this approach on three
benchmark classification tasks.
Exploring the Semantic Content of Unsupervised Graph Embeddings: An Empirical Study
Authors: Stephen Bonner, Ibad Kureshi, John Brennan, Georgios Theodoropoulos, Andrew Stephen McGough, Boguslaw Obara
Date: 2018-06-19 00:00:00
Tasks: Graph Embedding, Graph Mining

Graph embeddings have become a key and widely used technique within the field
of graph mining, proving to be successful across a broad range of domains
including social, citation, transportation and biological. Graph embedding
techniques aim to automatically create a low-dimensional representation of a
given graph, which captures key structural elements in the resulting embedding
space. However, to date, there has been little work exploring exactly which
topological structures are being learned in the embeddings process. In this
paper, we investigate if graph embeddings are approximating something analogous
with traditional vertex level graph features. If such a relationship can be
found, it could be used to provide a theoretical insight into how graph
embedding approaches function. We perform this investigation by predicting
known topological features, using supervised and unsupervised methods, directly
from the embedding space. If a mapping between the embeddings and topological
features can be found, then we argue that the structural information
encapsulated by the features is represented in the embedding space. To explore
this, we present extensive experimental evaluation from five state-of-the-art
unsupervised graph embedding techniques, across a range of empirical graph
datasets, measuring a selection of topological features. We demonstrate that
several topological features are indeed being approximated by the embedding
space, allowing key insight into how graph embeddings create good
representations.
CSI-based Outdoor Localization for Massive MIMO: Experiments with a Learning Approach
Authors: Alexis Decurninge, Luis García Ordóñez, Paul Ferrand, He Gaoning, Li Bojie, Zhang Wei, Maxime Guillaud
Date: 2018-06-19 00:00:00
Tasks: Outdoor Localization

We report on experimental results on the use of a learning-based approach to
infer the location of a mobile user of a cellular network within a cell, for a
5G-type Massive multiple input, multiple output (MIMO) system. We describe how
the sample spatial covariance matrix computed from the CSI can be used as the
input to a learning algorithm which attempts to relate it to user location. We
discuss several learning approaches, and analyze in depth the application of
extreme learning machines, for which theoretical approximate performance
benchmarks are available, to the localization problem. We validate the proposed
approach using experimental data collected on a Huawei 5G testbed, provide some
performance and robustness benchmarks, and discuss practical issues related to
the deployment of such a technique in 5G networks.
HybridNet: Integrating Model-based and Data-driven Learning to Predict Evolution of Dynamical Systems
Authors: Yun Long, Xueyuan She, Saibal Mukhopadhyay
Date: 2018-06-19 00:00:00

The robotic systems continuously interact with complex dynamical systems in
the physical world. Reliable predictions of spatiotemporal evolution of these
dynamical systems, with limited knowledge of system dynamics, are crucial for
autonomous operation. In this paper, we present HybridNet, a framework that
integrates data-driven deep learning and model-driven computation to reliably
predict spatiotemporal evolution of a dynamical systems even with in-exact
knowledge of their parameters. A data-driven deep neural network (DNN) with
Convolutional LSTM (ConvLSTM) as the backbone is employed to predict the
time-varying evolution of the external forces/perturbations. On the other hand,
the model-driven computation is performed using Cellular Neural Network (CeNN),
a neuro-inspired algorithm to model dynamical systems defined by coupled
partial differential equations (PDEs). CeNN converts the intricate numerical
computation into a series of convolution operations, enabling a trainable PDE
solver. With a feedback control loop, HybridNet can learn the physical
parameters governing the system's dynamics in real-time, and accordingly adapt
the computation models to enhance prediction accuracy for time-evolving
dynamical systems. The experimental results on two dynamical systems, namely,
heat convection-diffusion system, and fluid dynamical system, demonstrate that
the HybridNet produces higher accuracy than the state-of-the-art deep learning
based approach.
RISE: Randomized Input Sampling for Explanation of Black-box Models
Authors: Vitali Petsiuk, Abir Das, Kate Saenko
Date: 2018-06-19 00:00:00
Tasks: Explainable Artificial Intelligence (XAI), Feature Importance, Image Attribution, Image Captioning, Image Classification, Interpretability Techniques for Deep Learning, Interpretable Machine Learning

Deep neural networks are being used increasingly to automate data analysis
and decision making, yet their decision-making process is largely unclear and
is difficult to explain to the end users. In this paper, we address the problem
of Explainable AI for deep neural networks that take images as input and output
a class probability. We propose an approach called RISE that generates an
importance map indicating how salient each pixel is for the model's prediction.
In contrast to white-box approaches that estimate pixel importance using
gradients or other internal network state, RISE works on black-box models. It
estimates importance empirically by probing the model with randomly masked
versions of the input image and obtaining the corresponding outputs. We compare
our approach to state-of-the-art importance extraction methods using both an
automatic deletion/insertion metric and a pointing metric based on
human-annotated object segments. Extensive experiments on several benchmark
datasets show that our approach matches or exceeds the performance of other
methods, including white-box approaches.
  Project page: http://cs-people.bu.edu/vpetsiuk/rise/
Improved Image Selection for Stack-Based HDR Imaging
Authors: Peter van Beek
Date: 2018-06-19 00:00:00

Stack-based high dynamic range (HDR) imaging is a technique for achieving a
larger dynamic range in an image by combining several low dynamic range images
acquired at different exposures. Minimizing the set of images to combine, while
ensuring that the resulting HDR image fully captures the scene's irradiance, is
important to avoid long image acquisition and post-processing times. The
problem of selecting the set of images has received much attention. However,
existing methods either are not fully automatic, can be slow, or can fail to
fully capture more challenging scenes. In this paper, we propose a fully
automatic method for selecting the set of exposures to acquire that is both
fast and more accurate. We show on an extensive set of benchmark scenes that
our proposed method leads to improved HDR images as measured against ground
truth using the mean squared error, a pixel-based metric, and a visible
difference predictor and a quality score, both perception-based metrics.
Fast CapsNet for Lung Cancer Screening
Authors: Aryan Mobiny, Hien Van Nguyen
Date: 2018-06-19 00:00:00
Tasks: Computational Efficiency, Computed Tomography (CT), Decoder, General Classification, Image Reconstruction, Lung Nodule Classification

Lung cancer is the leading cause of cancer-related deaths in the past several
years. A major challenge in lung cancer screening is the detection of lung
nodules from computed tomography (CT) scans. State-of-the-art approaches in
automated lung nodule classification use deep convolutional neural networks
(CNNs). However, these networks require a large number of training samples to
generalize well. This paper investigates the use of capsule networks (CapsNets)
as an alternative to CNNs. We show that CapsNets significantly outperforms CNNs
when the number of training samples is small. To increase the computational
efficiency, our paper proposes a consistent dynamic routing mechanism that
results in $3\times$ speedup of CapsNet. Finally, we show that the original
image reconstruction method of CapNets performs poorly on lung nodule data. We
propose an efficient alternative, called convolutional decoder, that yields
lower reconstruction error and higher classification accuracy.
Built-in Vulnerabilities to Imperceptible Adversarial Perturbations
Authors: Thomas Tanay, Jerone T. A. Andrews, Lewis D. Griffin
Date: 2018-06-19 00:00:00
Tasks: Decoder

Designing models that are robust to small adversarial perturbations of their inputs has proven remarkably difficult. In this work we show that the reverse problem---making models more vulnerable---is surprisingly easy. After presenting some proofs of concept on MNIST, we introduce a generic tilting attack that injects vulnerabilities into the linear layers of pre-trained networks by increasing their sensitivity to components of low variance in the training data without affecting their performance on test data. We illustrate this attack on a multilayer perceptron trained on SVHN and use it to design a stand-alone adversarial module which we call a steganogram decoder. Finally, we show on CIFAR-10 that a poisoning attack with a poisoning rate as low as 0.1% can induce vulnerabilities to chosen imperceptible backdoor signals in state-of-the-art networks. Beyond their practical implications, these different results shed new light on the nature of the adversarial example phenomenon.
Speaker Adapted Beamforming for Multi-Channel Automatic Speech Recognition
Authors: Tobias Menne, Ralf Schlüter, Hermann Ney
Date: 2018-06-19 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

This paper presents, in the context of multi-channel ASR, a method to adapt a
mask based, statistically optimal beamforming approach to a speaker of
interest. The beamforming vector of the statistically optimal beamformer is
computed by utilizing speech and noise masks, which are estimated by a neural
network. The proposed adaptation approach is based on the integration of the
beamformer, which includes the mask estimation network, and the acoustic model
of the ASR system. This allows for the propagation of the training error, from
the acoustic modeling cost function, all the way through the beamforming
operation and through the mask estimation network. By using the results of a
first pass recognition and by keeping all other parameters fixed, the mask
estimation network can therefore be fine tuned by retraining. Utterances of a
speaker of interest can thus be used in a two pass approach, to optimize the
beamforming for the speech characteristics of that specific speaker. It is
shown that this approach improves the ASR performance of a state-of-the-art
multi-channel ASR system on the CHiME-4 data. Furthermore the effect of the
adaptation on the estimated speech masks is discussed.
Contrastive Hebbian Learning with Random Feedback Weights
Authors: Georgios Detorakis, Travis Bartley, Emre Neftci
Date: 2018-06-19 00:00:00

Neural networks are commonly trained to make predictions through learning
algorithms. Contrastive Hebbian learning, which is a powerful rule inspired by
gradient backpropagation, is based on Hebb's rule and the contrastive
divergence algorithm. It operates in two phases, the forward (or free) phase,
where the data are fed to the network, and a backward (or clamped) phase, where
the target signals are clamped to the output layer of the network and the
feedback signals are transformed through the transpose synaptic weight
matrices. This implies symmetries at the synaptic level, for which there is no
evidence in the brain. In this work, we propose a new variant of the algorithm,
called random contrastive Hebbian learning, which does not rely on any synaptic
weights symmetries. Instead, it uses random matrices to transform the feedback
signals during the clamped phase, and the neural dynamics are described by
first order non-linear differential equations. The algorithm is experimentally
verified by solving a Boolean logic task, classification tasks (handwritten
digits and letters), and an autoencoding task. This article also shows how the
parameters affect learning, especially the random matrices. We use the
pseudospectra analysis to investigate further how random matrices impact the
learning process. Finally, we discuss the biological plausibility of the
proposed algorithm, and how it can give rise to better computational models for
learning.
How to Maximize the Spread of Social Influence: A Survey
Authors: Giuseppe De Nittis, Nicola Gatti
Date: 2018-06-19 00:00:00
Tasks: Survey

This survey presents the main results achieved for the influence maximization
problem in social networks. This problem is well studied in the literature and,
thanks to its recent applications, some of which currently deployed on the
field, it is receiving more and more attention in the scientific community. The
problem can be formulated as follows: given a graph, with each node having a
certain probability of influencing its neighbors, select a subset of vertices
so that the number of nodes in the network that are influenced is maximized.
Starting from this model, we introduce the main theoretical developments and
computational results that have been achieved, taking into account different
diffusion models describing how the information spreads throughout the network,
various ways in which the sources of information could be placed, and how to
tackle the problem in the presence of uncertainties affecting the network.
Finally, we present one of the main application that has been developed and
deployed exploiting tools and techniques previously discussed.