pwc-archive/papers-with-abstracts huggingface dataset:
Learning Myelin Content in Multiple Sclerosis from Multimodal MRI through Adversarial Training
Authors: Wen Wei, Emilie Poirion, Benedetta Bodini, Stanley Durrleman, Nicholas Ayache, Bruno Stankoff, Olivier Colliot
Date: 2018-04-21 00:00:00
Tasks: Management

Multiple sclerosis (MS) is a demyelinating disease of the central nervous
system (CNS). A reliable measure of the tissue myelin content is therefore
essential for the understanding of the physiopathology of MS, tracking
progression and assessing treatment efficacy. Positron emission tomography
(PET) with $[^{11} \mbox{C}] \mbox{PIB}$ has been proposed as a promising
biomarker for measuring myelin content changes in-vivo in MS. However, PET
imaging is expensive and invasive due to the injection of a radioactive tracer.
On the contrary, magnetic resonance imaging (MRI) is a non-invasive, widely
available technique, but existing MRI sequences do not provide, to date, a
reliable, specific, or direct marker of either demyelination or remyelination.
In this work, we therefore propose Sketcher-Refiner Generative Adversarial
Networks (GANs) with specifically designed adversarial loss functions to
predict the PET-derived myelin content map from a combination of MRI
modalities. The prediction problem is solved by a sketch-refinement process in
which the sketcher generates the preliminary anatomical and physiological
information and the refiner refines and generates images reflecting the tissue
myelin content in the human brain. We evaluated the ability of our method to
predict myelin content at both global and voxel-wise levels. The evaluation
results show that the demyelination in lesion regions and myelin content in
normal-appearing white matter (NAWM) can be well predicted by our method. The
method has the potential to become a useful tool for clinical management of
patients with MS.
Localized Structured Prediction
Authors: Carlo Ciliberto, Francis Bach, Alessandro Rudi
Date: 2018-06-06 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Learning Theory, Prediction, Structured Prediction

Key to structured prediction is exploiting the problem structure to simplify the learning process. A major challenge arises when data exhibit a local structure (e.g., are made by "parts") that can be leveraged to better approximate the relation between (parts of) the input and (parts of) the output. Recent literature on signal processing, and in particular computer vision, has shown that capturing these aspects is indeed essential to achieve state-of-the-art performance. While such algorithms are typically derived on a case-by-case basis, in this work we propose the first theoretical framework to deal with part-based data from a general perspective. We derive a novel approach to deal with these problems and study its generalization properties within the setting of statistical learning theory. Our analysis is novel in that it explicitly quantifies the benefits of leveraging the part-based structure of the problem with respect to the learning rates of the proposed estimator.
Deep multi-scale architectures for monocular depth estimation
Authors: Michel Moukari, Sylvaine Picard, Loic Simon, Frédéric Jurie
Date: 2018-06-08 00:00:00
Tasks: Depth Estimation, Monocular Depth Estimation

This paper aims at understanding the role of multi-scale information in the
estimation of depth from monocular images. More precisely, the paper
investigates four different deep CNN architectures, designed to explicitly make
use of multi-scale features along the network, and compare them to a
state-of-the-art single-scale approach. The paper also shows that involving
multi-scale features in depth estimation not only improves the performance in
terms of accuracy, but also gives qualitatively better depth maps. Experiments
are done on the widely used NYU Depth dataset, on which the proposed method
achieves state-of-the-art performance.
Neonatal EEG Interpretation and Decision Support Framework for Mobile Platforms
Authors: Mark O'Sullivan, Sergi Gomez, Alison O'Shea, Eduard Salgado, Kevin Huillca, Sean Mathieson, Geraldine Boylan, Emanuel Popovici, Andriy Temko
Date: 2018-06-08 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

This paper proposes and implements an intuitive and pervasive solution for
neonatal EEG monitoring assisted by sonification and deep learning AI that
provides information about neonatal brain health to all neonatal healthcare
professionals, particularly those without EEG interpretation expertise. The
system aims to increase the demographic of clinicians capable of diagnosing
abnormalities in neonatal EEG. The proposed system uses a low-cost and
low-power EEG acquisition system. An Android app provides single-channel EEG
visualization, traffic-light indication of the presence of neonatal seizures
provided by a trained, deep convolutional neural network and an algorithm for
EEG sonification, designed to facilitate the perception of changes in EEG
morphology specific to neonatal seizures. The multifaceted EEG interpretation
framework is presented and the implemented mobile platform architecture is
analyzed with respect to its power consumption and accuracy.
Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory
Authors: Ron Amit, Ron Meir
Date: 2017-11-03 00:00:00
Proceeding: ICML 2018 7
Tasks: Meta-Learning

In meta-learning an agent extracts knowledge from observed tasks, aiming to facilitate learning of novel future tasks. Under the assumption that future tasks are 'related' to previous tasks, the accumulated knowledge should be learned in a way which captures the common structure across learned tasks, while allowing the learner sufficient flexibility to adapt to novel aspects of new tasks. We present a framework for meta-learning that is based on generalization error bounds, allowing us to extend various PAC-Bayes bounds to meta-learning. Learning takes place through the construction of a distribution over hypotheses based on the observed tasks, and its utilization for learning a new task. Thus, prior knowledge is incorporated through setting an experience-dependent prior for novel tasks. We develop a gradient-based algorithm which minimizes an objective function derived from the bounds and demonstrate its effectiveness numerically with deep neural networks. In addition to establishing the improved performance available through meta-learning, we demonstrate the intuitive way by which prior information is manifested at different levels of the network.
Investigating the Impact of CNN Depth on Neonatal Seizure Detection Performance
Authors: Alison O'Shea, Gordon Lightbody, Geraldine Boylan, Andriy Temko
Date: 2018-06-08 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Seizure Detection

This study presents a novel, deep, fully convolutional architecture which is
optimized for the task of EEG-based neonatal seizure detection. Architectures
of different depths were designed and tested; varying network depth impacts
convolutional receptive fields and the corresponding learned feature
complexity. Two deep convolutional networks are compared with a shallow
SVM-based neonatal seizure detector, which relies on the extraction of
hand-crafted features. On a large clinical dataset, of over 800 hours of
multichannel unedited EEG, containing 1389 seizure events, the deep 11-layer
architecture significantly outperforms the shallower architectures, improving
the AUC90 from 82.6% to 86.8%. Combining the end-to-end deep architecture with
the feature-based shallow SVM further improves the AUC90 to 87.6%. The fusion
of classifiers of different depths gives greatly improved performance and
reduced variability, making the combined classifier more clinically reliable.
Anonymous Walk Embeddings
Authors: Sergey Ivanov, Evgeny Burnaev
Date: 2018-05-30 00:00:00
Proceeding: ICML 2018 7
Tasks: General Classification, Graph Classification

The task of representing entire graphs has seen a surge of prominent results,
mainly due to learning convolutional neural networks (CNNs) on graph-structured
data. While CNNs demonstrate state-of-the-art performance in graph
classification task, such methods are supervised and therefore steer away from
the original problem of network representation in task-agnostic manner. Here,
we coherently propose an approach for embedding entire graphs and show that our
feature representations with SVM classifier increase classification accuracy of
CNN algorithms and traditional graph kernels. For this we describe a recently
discovered graph object, anonymous walk, on which we design task-independent
algorithms for learning graph representations in explicit and distributed way.
Overall, our work represents a new scalable unsupervised learning of
state-of-the-art representations of entire graphs.
Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions
Authors: Quynh Nguyen, Mahesh Chandra Mukkamala, Matthias Hein
Date: 2018-02-28 00:00:00
Proceeding: ICML 2018 7

In the recent literature the important role of depth in deep learning has
been emphasized. In this paper we argue that sufficient width of a feedforward
network is equally important by answering the simple question under which
conditions the decision regions of a neural network are connected. It turns out
that for a class of activation functions including leaky ReLU, neural networks
having a pyramidal structure, that is no layer has more hidden units than the
input dimension, produce necessarily connected decision regions. This implies
that a sufficiently wide hidden layer is necessary to guarantee that the
network can produce disconnected decision regions. We discuss the implications
of this result for the construction of neural networks, in particular the
relation to the problem of adversarial manipulation of classifiers.
Unsupervised Feature Learning Toward a Real-time Vehicle Make and Model Recognition
Authors: Amir Nazemi, Mohammad Javad Shafiee, Zohreh Azimifar, Alexander Wong
Date: 2018-06-08 00:00:00

Vehicle Make and Model Recognition (MMR) systems provide a fully automatic
framework to recognize and classify different vehicle models. Several
approaches have been proposed to address this challenge, however they can
perform in restricted conditions. Here, we formulate the vehicle make and model
recognition as a fine-grained classification problem and propose a new
configurable on-road vehicle make and model recognition framework. We benefit
from the unsupervised feature learning methods and in more details we employ
Locality constraint Linear Coding (LLC) method as a fast feature encoder for
encoding the input SIFT features. The proposed method can perform in real
environments of different conditions. This framework can recognize fifty models
of vehicles and has an advantage to classify every other vehicle not belonging
to one of the specified fifty classes as an unknown vehicle. The proposed MMR
framework can be configured to become faster or more accurate based on the
application domain. The proposed approach is examined on two datasets including
Iranian on-road vehicle dataset and CompuCar dataset. The Iranian on-road
vehicle dataset contains images of 50 models of vehicles captured in real
situations by traffic cameras in different weather and lighting conditions.
Experimental results show superiority of the proposed framework over the
state-of-the-art methods on Iranian on-road vehicle datatset and comparable
results on CompuCar dataset with 97.5% and 98.4% accuracies, respectively.
Generating Image Sequence from Description with LSTM Conditional GAN
Authors: Xu Ouyang, Xi Zhang, Di Ma, Gady Agam
Date: 2018-06-08 00:00:00

Generating images from word descriptions is a challenging task. Generative
adversarial networks(GANs) are shown to be able to generate realistic images of
real-life objects. In this paper, we propose a new neural network architecture
of LSTM Conditional Generative Adversarial Networks to generate images of
real-life objects. Our proposed model is trained on the Oxford-102 Flowers and
Caltech-UCSD Birds-200-2011 datasets. We demonstrate that our proposed model
produces the better results surpassing other state-of-art approaches.
3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation
Authors: Masahiro Oda, Natsuki Shimizu, Holger R. Roth, Ken'ichi Karasawa, Takayuki Kitasaka, Kazunari Misawa, Michitaka Fujiwara, Daniel Rueckert, Kensaku MORI
Date: 2018-06-08 00:00:00
Tasks: Automated Pancreas Segmentation, Organ Segmentation, Pancreas Segmentation, Position, regression, Segmentation

This paper presents a fully automated atlas-based pancreas segmentation
method from CT volumes utilizing 3D fully convolutional network (FCN)
feature-based pancreas localization. Segmentation of the pancreas is difficult
because it has larger inter-patient spatial variations than other organs.
Previous pancreas segmentation methods failed to deal with such variations. We
propose a fully automated pancreas segmentation method that contains novel
localization and segmentation. Since the pancreas neighbors many other organs,
its position and size are strongly related to the positions of the surrounding
organs. We estimate the position and the size of the pancreas (localized) from
global features by regression forests. As global features, we use intensity
differences and 3D FCN deep learned features, which include automatically
extracted essential features for segmentation. We chose 3D FCN features from a
trained 3D U-Net, which is trained to perform multi-organ segmentation. The
global features include both the pancreas and surrounding organ information.
After localization, a patient-specific probabilistic atlas-based pancreas
segmentation is performed. In evaluation results with 146 CT volumes, we
achieved 60.6% of the Jaccard index and 73.9% of the Dice overlap.
Machine learning-based colon deformation estimation method for colonoscope tracking
Authors: Masahiro Oda, Takayuki Kitasaka, Kazuhiro Furukawa, Ryoji Miyahara, Yoshiki Hirooka, Hidemi Goto, Nassir Navab, Kensaku MORI
Date: 2018-06-08 00:00:00
Tasks: BIG-bench Machine Learning, regression

This paper presents a colon deformation estimation method, which can be used
to estimate colon deformations during colonoscope insertions. Colonoscope
tracking or navigation system that navigates a physician to polyp positions
during a colonoscope insertion is required to reduce complications such as
colon perforation. A previous colonoscope tracking method obtains a colonoscope
position in the colon by registering a colonoscope shape and a colon shape. The
colonoscope shape is obtained using an electromagnetic sensor, and the colon
shape is obtained from a CT volume. However, large tracking errors were
observed due to colon deformations occurred during colonoscope insertions. Such
deformations make the registration difficult. Because the colon deformation is
caused by a colonoscope, there is a strong relationship between the colon
deformation and the colonoscope shape. An estimation method of colon
deformations occur during colonoscope insertions is necessary to reduce
tracking errors. We propose a colon deformation estimation method. This method
is used to estimate a deformed colon shape from a colonoscope shape. We use the
regression forests algorithm to estimate a deformed colon shape. The regression
forests algorithm is trained using pairs of colon and colonoscope shapes, which
contains deformations occur during colonoscope insertions. As a preliminary
study, we utilized the method to estimate deformations of a colon phantom. In
our experiments, the proposed method correctly estimated deformed colon phantom
shapes.
Domain Adaptive Generation of Aircraft on Satellite Imagery via Simulated and Unsupervised Learning
Authors: Junghoon Seo, Seunghyun Jeon, Taegyun Jeon
Date: 2018-06-08 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification, object-detection, Object Detection

Object detection and classification for aircraft are the most important tasks
in the satellite image analysis. The success of modern detection and
classification methods has been based on machine learning and deep learning.
One of the key requirements for those learning processes is huge data to train.
However, there is an insufficient portion of aircraft since the targets are on
military action and oper- ation. Considering the characteristics of satellite
imagery, this paper attempts to provide a framework of the simulated and
unsupervised methodology without any additional su- pervision or physical
assumptions. Finally, the qualitative and quantitative analysis revealed a
potential to replenish insufficient data for machine learning platform for
satellite image analysis.
Noise-adding Methods of Saliency Map as Series of Higher Order Partial Derivative
Authors: Junghoon Seo, Jeongyeol Choe, Jamyoung Koo, Seunghyeon Jeon, Beomsu Kim, Taegyun Jeon
Date: 2018-06-08 00:00:00

SmoothGrad and VarGrad are techniques that enhance the empirical quality of
standard saliency maps by adding noise to input. However, there were few works
that provide a rigorous theoretical interpretation of those methods. We
analytically formalize the result of these noise-adding methods. As a result,
we observe two interesting results from the existing noise-adding methods.
First, SmoothGrad does not make the gradient of the score function smooth.
Second, VarGrad is independent of the gradient of the score function. We
believe that our findings provide a clue to reveal the relationship between
local explanation methods of deep neural networks and higher-order partial
derivatives of the score function.
On Matching Pursuit and Coordinate Descent
Authors: Francesco Locatello, Anant Raj, Sai Praneeth Karimireddy, Gunnar Rätsch, Bernhard Schölkopf, Sebastian U. Stich, Martin Jaggi
Date: 2018-03-26 00:00:00
Proceeding: ICML 2018 7

Two popular examples of first-order optimization methods over linear spaces are coordinate descent and matching pursuit algorithms, with their randomized variants. While the former targets the optimization by moving along coordinates, the latter considers a generalized notion of directions. Exploiting the connection between the two algorithms, we present a unified analysis of both, providing affine invariant sublinear $\mathcal{O}(1/t)$ rates on smooth objectives and linear convergence on strongly convex objectives. As a byproduct of our affine invariant analysis of matching pursuit, our rates for steepest coordinate descent are the tightest known. Furthermore, we show the first accelerated convergence rate $\mathcal{O}(1/t^2)$ for matching pursuit and steepest coordinate descent on convex objectives.
Logarithmic mathematical morphology: a new framework adaptive to illumination changes
Authors: Guillaume Noyel
Date: 2018-06-08 00:00:00

A new set of mathematical morphology (MM) operators adaptive to illumination
changes caused by variation of exposure time or light intensity is defined
thanks to the Logarithmic Image Processing (LIP) model. This model based on the
physics of acquisition is consistent with human vision. The fundamental
operators, the logarithmic-dilation and the logarithmic-erosion, are defined
with the LIP-addition of a structuring function. The combination of these two
adjunct operators gives morphological filters, namely the logarithmic-opening
and closing, useful for pattern recognition. The mathematical relation existing
between ``classical'' dilation and erosion and their logarithmic-versions is
established facilitating their implementation. Results on simulated and real
images show that logarithmic-MM is more efficient on low-contrasted information
than ``classical'' MM.
q-Space Novelty Detection with Variational Autoencoders
Authors: Aleksei Vasilev, Vladimir Golkov, Marc Meissner, Ilona Lipp, Eleonora Sgarlata, Valentina Tomassini, Derek K. Jones, Daniel Cremers
Date: 2018-06-08 00:00:00
Tasks: Diffusion  MRI, Novelty Detection

In machine learning, novelty detection is the task of identifying novel
unseen data. During training, only samples from the normal class are available.
Test samples are classified as normal or abnormal by assignment of a novelty
score. Here we propose novelty detection methods based on training variational
autoencoders (VAEs) on normal data. Since abnormal samples are not used during
training, we define novelty metrics based on the (partially complementary)
assumptions that the VAE is less capable of reconstructing abnormal samples
well; that abnormal samples more strongly violate the VAE regularizer; and that
abnormal samples differ from normal samples not only in input-feature space,
but also in the VAE latent space and VAE output. These approaches, combined
with various possibilities of using (e.g. sampling) the probabilistic VAE to
obtain scalar novelty scores, yield a large family of methods. We apply these
methods to magnetic resonance imaging, namely to the detection of
diffusion-space (q-space) abnormalities in diffusion MRI scans of multiple
sclerosis patients, i.e. to detect multiple sclerosis lesions without using any
lesion labels for training. Many of our methods outperform previously proposed
q-space novelty detection methods. We also evaluate the proposed methods on the
MNIST handwritten digits dataset and show that many of them are able to
outperform the state of the art.
Towards Binary-Valued Gates for Robust LSTM Training
Authors: Zhuohan Li, Di He, Fei Tian, Wei Chen, Tao Qin, Li-Wei Wang, Tie-Yan Liu
Date: 2018-06-08 00:00:00
Proceeding: ICML 2018 7

Long Short-Term Memory (LSTM) is one of the most widely used recurrent
structures in sequence modeling. It aims to use gates to control information
flow (e.g., whether to skip some information or not) in the recurrent
computations, although its practical implementation based on soft gates only
partially achieves this goal. In this paper, we propose a new way for LSTM
training, which pushes the output values of the gates towards 0 or 1. By doing
so, we can better control the information flow: the gates are mostly open or
closed, instead of in a middle state, which makes the results more
interpretable. Empirical studies show that (1) Although it seems that we
restrict the model capacity, there is no performance drop: we achieve better or
comparable performances due to its better generalization ability; (2) The
outputs of gates are not sensitive to their inputs: we can easily compress the
LSTM unit in multiple ways, e.g., low-rank approximation and low-precision
approximation. The compressed models are even better than the baseline models
without compression.
A Systematic Evaluation of Recent Deep Learning Architectures for Fine-Grained Vehicle Classification
Authors: Krassimir Valev, Arne Schumann, Lars Sommer, Jürgen Beyerer
Date: 2018-06-08 00:00:00
Tasks: Classification, Data Augmentation, Fine-Grained Vehicle Classification, General Classification, image-classification, Image Classification

Fine-grained vehicle classification is the task of classifying make, model,
and year of a vehicle. This is a very challenging task, because vehicles of
different types but similar color and viewpoint can often look much more
similar than vehicles of same type but differing color and viewpoint. Vehicle
make, model, and year in com- bination with vehicle color - are of importance
in several applications such as vehicle search, re-identification, tracking,
and traffic analysis. In this work we investigate the suitability of several
recent landmark convolutional neural network (CNN) architectures, which have
shown top results on large scale image classification tasks, for the task of
fine-grained classification of vehicles. We compare the performance of the
networks VGG16, several ResNets, Inception architectures, the recent DenseNets,
and MobileNet. For classification we use the Stanford Cars-196 dataset which
features 196 different types of vehicles. We investigate several aspects of CNN
training, such as data augmentation and training from scratch vs. fine-tuning.
Importantly, we introduce no aspects in the architectures or training process
which are specific to vehicle classification. Our final model achieves a
state-of-the-art classification accuracy of 94.6% outperforming all related
works, even approaches which are specifically tailored for the task, e.g. by
including vehicle part detections.