pwc-archive/papers-with-abstracts huggingface dataset:
Generative Adversarial Training for MRA Image Synthesis Using Multi-Contrast MRI
Authors: Sahin Olut, Yusuf Huseyin Sahin, Ugur Demir, Gozde Unal
Date: 2018-04-12 00:00:00
Tasks: Anatomy, Generative Adversarial Network, Image Generation

Magnetic Resonance Angiography (MRA) has become an essential MR contrast for
imaging and evaluation of vascular anatomy and related diseases. MRA
acquisitions are typically ordered for vascular interventions, whereas in
typical scenarios, MRA sequences can be absent in the patient scans. This
motivates the need for a technique that generates inexistent MRA from existing
MR multi-contrast, which could be a valuable tool in retrospective subject
evaluations and imaging studies. In this paper, we present a generative
adversarial network (GAN) based technique to generate MRA from T1-weighted and
T2-weighted MRI images, for the first time to our knowledge. To better model
the representation of vessels which the MRA inherently highlights, we design a
loss term dedicated to a faithful reproduction of vascularities. To that end,
we incorporate steerable filter responses of the generated and reference images
inside a Huber function loss term. Extending the well- established
generator-discriminator architecture based on the recent PatchGAN model with
the addition of steerable filter loss, the proposed steerable GAN (sGAN) method
is evaluated on the large public database IXI. Experimental results show that
the sGAN outperforms the baseline GAN method in terms of an overlap score with
similar PSNR values, while it leads to improved visual perceptual quality.
FPGA Implementations of 3D-SIMD Processor Architecture for Deep Neural Networks Using Relative Indexed Compressed Sparse Filter Encoding Format and Stacked Filters Stationary Flow
Authors: Yuechao Gao, Nianhong Liu, Sheng Zhang
Date: 2018-03-28 00:00:00

It is a challenging task to deploy computationally and memory intensive
State-of-the-art deep neural networks (DNNs) on embedded systems with limited
hardware resources and power budgets. Recently developed techniques like Deep
Compression make it possible to fit large DNNs, such as AlexNet and VGGNet,
fully in on-chip SRAM. But sparse networks compressed using existing encoding
formats, like CSR or CSC, complex the computation at runtime due to their
irregular memory access characteristics. In [1], we introduce a computation
dataflow, stacked filters stationary dataflow (SFS), and a corresponding data
encoding format, relative indexed compressed sparse filter format (CSF), to
make the best of data sparsity, and simplify data handling at execution time.
In this paper we present FPGA implementations of these methods. We implement
several compact streaming fully connected (FC) and Convolutional (CONV) neural
network processors to show their efficiency. Comparing with the
state-of-the-art results [2,3,4], our methods achieve at least 2x improvement
for computation efficiency per PE on most layers. Especially, our methods
achieve 8x improvement on AlexNet layer CONV4 with 384 filters, and 11x
improvement on VGG16 layer CONV5-3 with 512 filters.
A Recurrent CNN for Automatic Detection and Classification of Coronary Artery Plaque and Stenosis in Coronary CT Angiography
Authors: Majd Zreik, Robbert W. van Hamersvelt, Jelmer M. Wolterink, Tim Leiner, Max A. Viergever, Ivana Isgum
Date: 2018-04-12 00:00:00
Tasks: Classification, General Classification, Management, Multi-class Classification

Various types of atherosclerotic plaque and varying grades of stenosis could
lead to different management of patients with coronary artery disease.
Therefore, it is crucial to detect and classify the type of coronary artery
plaque, as well as to detect and determine the degree of coronary artery
stenosis. This study includes retrospectively collected clinically obtained
coronary CT angiography (CCTA) scans of 163 patients. To perform automatic
analysis for coronary artery plaque and stenosis classification, a multi-task
recurrent convolutional neural network is applied on multi-planar reformatted
(MPR) images of the coronary arteries. First, a 3D convolutional neural network
is utilized to extract features along the coronary artery. Subsequently, the
extracted features are aggregated by a recurrent neural network that performs
two simultaneous multi-class classification tasks. In the first task, the
network detects and characterizes the type of the coronary artery plaque (no
plaque, non-calcified, mixed, calcified). In the second task, the network
detects and determines the anatomical significance of the coronary artery
stenosis (no stenosis, non-significant i.e. <50% luminal narrowing, significant
i.e. >50% luminal narrowing). For detection and classification of coronary
plaque, the method achieved an accuracy of 0.77. For detection and
classification of stenosis, the method achieved an accuracy of 0.80. The
results demonstrate that automatic detection and classification of coronary
artery plaque and stenosis are feasible. This may enable automated triage of
patients to those without coronary plaque and those with coronary plaque and
stenosis in need for further cardiovascular workup.
Global SNR Estimation of Speech Signals using Entropy and Uncertainty Estimates from Dropout Networks
Authors: Rohith Aralikatti, Dilip Margam, Tanay Sharma, Thanda Abhinav, Shankar M Venkatesan
Date: 2018-04-12 00:00:00
Tasks: speech-recognition, Speech Recognition

This paper demonstrates two novel methods to estimate the global SNR of
speech signals. In both methods, Deep Neural Network-Hidden Markov Model
(DNN-HMM) acoustic model used in speech recognition systems is leveraged for
the additional task of SNR estimation. In the first method, the entropy of the
DNN-HMM output is computed. Recent work on bayesian deep learning has shown
that a DNN-HMM trained with dropout can be used to estimate model uncertainty
by approximating it as a deep Gaussian process. In the second method, this
approximation is used to obtain model uncertainty estimates. Noise specific
regressors are used to predict the SNR from the entropy and model uncertainty.
The DNN-HMM is trained on GRID corpus and tested on different noise profiles
from the DEMAND noise database at SNR levels ranging from -10 dB to 30 dB.
MMGAN: Manifold Matching Generative Adversarial Network
Authors: Noseong Park, Ankesh Anand, Joel Ruben Antony Moniz, Kookjin Lee, Tanmoy Chakraborty, Jaegul Choo, Hongkyu Park, Young-Min Kim
Date: 2017-07-26 00:00:00
Tasks: Generative Adversarial Network

It is well-known that GANs are difficult to train, and several different
techniques have been proposed in order to stabilize their training. In this
paper, we propose a novel training method called manifold-matching, and a new
GAN model called manifold-matching GAN (MMGAN). MMGAN finds two manifolds
representing the vector representations of real and fake images. If these two
manifolds match, it means that real and fake images are statistically
identical. To assist the manifold-matching task, we also use i) kernel tricks
to find better manifold structures, ii) moving-averaged manifolds across
mini-batches, and iii) a regularizer based on correlation matrix to suppress
mode collapse.
  We conduct in-depth experiments with three image datasets and compare with
several state-of-the-art GAN models. 32.4% of images generated by the proposed
MMGAN are recognized as fake images during our user study (16% enhancement
compared to other state-of-the-art model). MMGAN achieved an unsupervised
inception score of 7.8 for CIFAR-10.
A Topological Approach to Meta-heuristics: Analytical Results on the BFS vs. DFS Algorithm Selection Problem
Authors: Tom Everitt, Marcus Hutter
Date: 2015-09-09 00:00:00

Search is a central problem in artificial intelligence, and breadth-first
search (BFS) and depth-first search (DFS) are the two most fundamental ways to
search. In this paper we derive estimates for average BFS and DFS runtime. The
average runtime estimates can be used to allocate resources or judge the
hardness of a problem. They can also be used for selecting the best graph
representation, and for selecting the faster algorithm out of BFS and DFS. They
may also form the basis for an analysis of more advanced search methods. The
paper treats both tree search and graph search. For tree search, we employ a
probabilistic model of goal distribution; for graph search, the analysis
depends on an additional statistic of path redundancy and average branching
factor. As an application, we use the results to predict BFS and DFS runtime on
two concrete grammar problems and on the N-puzzle. Experimental verification
shows that our analytical approximations come close to empirical reality.
Development and Validation of Deep Learning Algorithms for Detection of Critical Findings in Head CT Scans
Authors: Sasank Chilamkurthy, Rohit Ghosh, Swetha Tanamala, Mustafa Biviji, Norbert G. Campeau, Vasantha Kumar Venugopal, Vidur Mahajan, Pooja Rao, Prashant Warier
Date: 2018-03-13 00:00:00

Importance: Non-contrast head CT scan is the current standard for initial
imaging of patients with head trauma or stroke symptoms.
  Objective: To develop and validate a set of deep learning algorithms for
automated detection of following key findings from non-contrast head CT scans:
intracranial hemorrhage (ICH) and its types, intraparenchymal (IPH),
intraventricular (IVH), subdural (SDH), extradural (EDH) and subarachnoid (SAH)
hemorrhages, calvarial fractures, midline shift and mass effect.
  Design and Settings: We retrospectively collected a dataset containing
313,318 head CT scans along with their clinical reports from various centers. A
part of this dataset (Qure25k dataset) was used to validate and the rest to
develop algorithms. Additionally, a dataset (CQ500 dataset) was collected from
different centers in two batches B1 & B2 to clinically validate the algorithms.
  Main Outcomes and Measures: Original clinical radiology report and consensus
of three independent radiologists were considered as gold standard for Qure25k
and CQ500 datasets respectively. Area under receiver operating characteristics
curve (AUC) for each finding was primarily used to evaluate the algorithms.
  Results: Qure25k dataset contained 21,095 scans (mean age 43.31; 42.87%
female) while batches B1 and B2 of CQ500 dataset consisted of 214 (mean age
43.40; 43.92% female) and 277 (mean age 51.70; 30.31% female) scans
respectively. On Qure25k dataset, the algorithms achieved AUCs of 0.9194,
0.8977, 0.9559, 0.9161, 0.9288 and 0.9044 for detecting ICH, IPH, IVH, SDH, EDH
and SAH respectively. AUCs for the same on CQ500 dataset were 0.9419, 0.9544,
0.9310, 0.9521, 0.9731 and 0.9574 respectively. For detecting calvarial
fractures, midline shift and mass effect, AUCs on Qure25k dataset were 0.9244,
0.9276 and 0.8583 respectively, while AUCs on CQ500 dataset were 0.9624, 0.9697
and 0.9216 respectively.
A two-stage 3D Unet framework for multi-class segmentation on full resolution image
Authors: Chengjia Wang, Tom MacGillivray, Gillian Macnaught, Guang Yang, David Newby
Date: 2018-04-12 00:00:00
Tasks: Image Super-Resolution, Segmentation, Super-Resolution

Deep convolutional neural networks (CNNs) have been intensively used for
multi-class segmentation of data from different modalities and achieved
state-of-the-art performances. However, a common problem when dealing with
large, high resolution 3D data is that the volumes input into the deep CNNs has
to be either cropped or downsampled due to limited memory capacity of computing
devices. These operations lead to loss of resolution and increment of class
imbalance in the input data batches, which can downgrade the performances of
segmentation algorithms. Inspired by the architecture of image super-resolution
CNN (SRCNN) and self-normalization network (SNN), we developed a two-stage
modified Unet framework that simultaneously learns to detect a ROI within the
full volume and to classify voxels without losing the original resolution.
Experiments on a variety of multi-modal volumes demonstrated that, when trained
with a simply weighted dice coefficients and our customized learning procedure,
this framework shows better segmentation performances than state-of-the-art
Deep CNNs with advanced similarity metrics.
Zero-Shot Object Detection
Authors: Ankan Bansal, Karan Sikka, Gaurav Sharma, Rama Chellappa, Ajay Divakaran
Date: 2018-04-12 00:00:00
Proceeding: ECCV 2018 9
Tasks: Object, object-detection, Object Detection, zero-shot-classification, Zero-Shot Learning, Zero-Shot Object Detection

We introduce and tackle the problem of zero-shot object detection (ZSD),
which aims to detect object classes which are not observed during training. We
work with a challenging set of object classes, not restricting ourselves to
similar and/or fine-grained categories as in prior works on zero-shot
classification. We present a principled approach by first adapting
visual-semantic embeddings for ZSD. We then discuss the problems associated
with selecting a background class and motivate two background-aware approaches
for learning robust detectors. One of these models uses a fixed background
class and the other is based on iterative latent assignments. We also outline
the challenge associated with using a limited number of training classes and
propose a solution based on dense sampling of the semantic label space using
auxiliary data with a large number of categories. We propose novel splits of
two standard detection datasets - MSCOCO and VisualGenome, and present
extensive empirical results in both the traditional and generalized zero-shot
settings to highlight the benefits of the proposed methods. We provide useful
insights into the algorithm and conclude by posing some open questions to
encourage further research.
Benchmark data and method for real-time people counting in cluttered scenes using depth sensors
Authors: Shi-Jie Sun, Naveed Akhtar, HuanSheng Song, Chaoyang Zhang, Jian-Xin Li, Ajmal Mian
Date: 2018-04-12 00:00:00
Tasks: Benchmarking

Vision-based automatic counting of people has widespread applications in
intelligent transportation systems, security, and logistics. However, there is
currently no large-scale public dataset for benchmarking approaches on this
problem. This work fills this gap by introducing the first real-world RGB-D
People Counting DataSet (PCDS) containing over 4,500 videos recorded at the
entrance doors of buses in normal and cluttered conditions. It also proposes an
efficient method for counting people in real-world cluttered scenes related to
public transportations using depth videos. The proposed method computes a point
cloud from the depth video frame and re-projects it onto the ground plane to
normalize the depth information. The resulting depth image is analyzed for
identifying potential human heads. The human head proposals are meticulously
refined using a 3D human model. The proposals in each frame of the continuous
video stream are tracked to trace their trajectories. The trajectories are
again refined to ascertain reliable counting. People are eventually counted by
accumulating the head trajectories leaving the scene. To enable effective head
and trajectory identification, we also propose two different compound features.
A thorough evaluation on PCDS demonstrates that our technique is able to count
people in cluttered scenes with high accuracy at 45 fps on a 1.7 GHz processor,
and hence it can be deployed for effective real-time people counting for
intelligent transportation systems.
MelanoGANs: High Resolution Skin Lesion Synthesis with GANs
Authors: Christoph Baur, Shadi Albarqouni, Nassir Navab
Date: 2018-04-12 00:00:00
Tasks: Image Generation, Lesion Classification, Skin Lesion Classification, Vocal Bursts Intensity Prediction

Generative Adversarial Networks (GANs) have been successfully used to
synthesize realistically looking images of faces, scenery and even medical
images. Unfortunately, they usually require large training datasets, which are
often scarce in the medical field, and to the best of our knowledge GANs have
been only applied for medical image synthesis at fairly low resolution.
However, many state-of-the-art machine learning models operate on high
resolution data as such data carries indispensable, valuable information. In
this work, we try to generate realistically looking high resolution images of
skin lesions with GANs, using only a small training dataset of 2000 samples.
The nature of the data allows us to do a direct comparison between the image
statistics of the generated samples and the real dataset. We both
quantitatively and qualitatively compare state-of-the-art GAN architectures
such as DCGAN and LAPGAN against a modification of the latter for the task of
image generation at a resolution of 256x256px. Our investigation shows that we
can approximate the real data distribution with all of the models, but we
notice major differences when visually rating sample realism, diversity and
artifacts. In a set of use-case experiments on skin lesion classification, we
further show that we can successfully tackle the problem of heavy class
imbalance with the help of synthesized high resolution melanoma samples.
A Hierarchical Latent Structure for Variational Conversation Modeling
Authors: Yookoon Park, Jaemin Cho, Gunhee Kim
Date: 2018-04-10 00:00:00
Proceeding: NAACL 2018 6

Variational autoencoders (VAE) combined with hierarchical RNNs have emerged
as a powerful framework for conversation modeling. However, they suffer from
the notorious degeneration problem, where the decoders learn to ignore latent
variables and reduce to vanilla RNNs. We empirically show that this degeneracy
occurs mostly due to two reasons. First, the expressive power of hierarchical
RNN decoders is often high enough to model the data using only its decoding
distributions without relying on the latent variables. Second, the conditional
VAE structure whose generation process is conditioned on a context, makes the
range of training targets very sparse; that is, the RNN decoders can easily
overfit to the training data ignoring the latent variables. To solve the
degeneration problem, we propose a novel model named Variational Hierarchical
Conversation RNNs (VHCR), involving two key ideas of (1) using a hierarchical
structure of latent variables, and (2) exploiting an utterance drop
regularization. With evaluations on two datasets of Cornell Movie Dialog and
Ubuntu Dialog Corpus, we show that our VHCR successfully utilizes latent
variables and outperforms state-of-the-art models for conversation generation.
Moreover, it can perform several new utterance control tasks, thanks to its
hierarchical latent structure.
Correcting differences in multi-site neuroimaging data using Generative Adversarial Networks
Authors: Harrison Nguyen, Richard W. Morris, Anthony W. Harris, Mayuresh S. Korgoankar, Fabio Ramos
Date: 2018-03-26 00:00:00

Magnetic Resonance Imaging (MRI) of the brain has been used to investigate a
wide range of neurological disorders, but data acquisition can be expensive,
time-consuming, and inconvenient. Multi-site studies present a valuable
opportunity to advance research by pooling data in order to increase
sensitivity and statistical power. However images derived from MRI are
susceptible to both obvious and non-obvious differences between sites which can
introduce bias and subject variance, and so reduce statistical power. To
rectify these differences, we propose a data driven approach using a deep
learning architecture known as generative adversarial networks (GANs). GANs
learn to estimate two distributions, and can then be used to transform examples
from one distribution into the other distribution. Here we transform
T1-weighted brain images collected from two different sites into MR images from
the same site. We evaluate whether our model can reduce site-specific
differences without loss of information related to gender (male, female) or
clinical diagnosis (schizophrenia, bipolar disorder, healthy). When trained
appropriately, our model is able to normalise imaging sets to a common scanner
set with less information loss compared to current approaches. An important
advantage is our method can be treated as a black box that does not require any
knowledge of the sources of bias but only needs at least two distinct imaging
sets.
Local reservoir model for choice-based learning
Authors: Makoto Naruse, Eiji Yamamoto, Takashi Nakao, Takuma Akimoto, Hayato Saigo, Kazuya Okamura, Izumi Ojima, Georg Northoff, Hirokazu Hori
Date: 2018-04-12 00:00:00
Tasks: Decision Making, model

Decision making based on behavioral and neural observations of living systems
has been extensively studied in brain science, psychology, and other
disciplines. Decision-making mechanisms have also been experimentally
implemented in physical processes, such as single photons and chaotic lasers.
The findings of these experiments suggest that there is a certain common basis
in describing decision making, regardless of its physical realizations. In this
study, we propose a local reservoir model to account for choice-based learning
(CBL). CBL describes decision consistency as a phenomenon where making a
certain decision increases the possibility of making that same decision again
later, which has been intensively investigated in neuroscience, psychology,
etc. Our proposed model is inspired by the viewpoint that a decision is
affected by its local environment, which is referred to as a local reservoir.
If the size of the local reservoir is large enough, consecutive decision making
will not be affected by previous decisions, thus showing lower degrees of
decision consistency in CBL. In contrast, if the size of the local reservoir
decreases, a biased distribution occurs within it, which leads to higher
degrees of decision consistency in CBL. In this study, an analytical approach
on local reservoirs is presented, as well as several numerical demonstrations.
Furthermore, a physical architecture for CBL based on single photons is
discussed, and the effects of local reservoirs is numerically demonstrated.
Decision consistency in human decision-making tasks and in recruiting empirical
data are evaluated based on local reservoir. In summary, the proposed local
reservoir model paves a path toward establishing a foundation for computational
mechanisms and the systematic analysis of decision making on different levels.
Entangled-photon decision maker
Authors: Nicolas Chauvet, David Jegouso, Benoît Boulanger, Hayato Saigo, Kazuya Okamura, Hirokazu Hori, Aurélien Drezet, Serge Huant, Guillaume Bachelier, Makoto Naruse
Date: 2018-04-12 00:00:00
Tasks: Decision Making

The competitive multi-armed bandit (CMAB) problem is related to social issues such as maximizing total social benefits while preserving equality among individuals by overcoming conflicts between individual decisions, which could seriously decrease social benefits. The study described herein provides experimental evidence that entangled photons physically resolve the CMAB in the 2-arms 2-players case, maximizing the social rewards while ensuring equality. Moreover, we demonstrated that deception, or outperforming the other player by receiving a greater reward, cannot be accomplished in a polarization-entangled-photon-based system, while deception is achievable in systems based on classical polarization-correlated photons with fixed polarizations. Besides, random polarization-correlated photons have been studied numerically and shown to ensure equality between players and deception prevention as well, although the CMAB maximum performance is reduced as compared with entangled photon experiments. Autonomous alignment schemes for polarization bases were also experimentally demonstrated based only on decision conflict information observed by an individual without communications between players. This study paves a way for collective decision making in uncertain dynamically changing environments based on entangled quantum states, a crucial step toward utilizing quantum systems for intelligent functionalities.