pwc-archive/papers-with-abstracts huggingface dataset:
Three for one and one for three: Flow, Segmentation, and Surface Normals
Authors: Hoang-An Le, Anil S. Baslamisli, Thomas Mensink, Theo Gevers
Date: 2018-07-19 00:00:00
Tasks: Optical Flow Estimation, Scene Understanding, Segmentation, Semantic Segmentation

Optical flow, semantic segmentation, and surface normals represent different
information modalities, yet together they bring better cues for scene
understanding problems. In this paper, we study the influence between the three
modalities: how one impacts on the others and their efficiency in combination.
We employ a modular approach using a convolutional refinement network which is
trained supervised but isolated from RGB images to enforce joint modality
features. To assist the training process, we create a large-scale synthetic
outdoor dataset that supports dense annotation of semantic segmentation,
optical flow, and surface normals. The experimental results show positive
influence among the three modalities, especially for objects' boundaries,
region consistency, and scene structures.
Guided Upsampling Network for Real-Time Semantic Segmentation
Authors: Davide Mazzini
Date: 2018-07-19 00:00:00
Tasks: Decoder, Real-Time Semantic Segmentation, Segmentation, Semantic Segmentation

Semantic segmentation architectures are mainly built upon an encoder-decoder
structure. These models perform subsequent downsampling operations in the
encoder. Since operations on high-resolution activation maps are
computationally expensive, usually the decoder produces output segmentation
maps by upsampling with parameters-free operators like bilinear or
nearest-neighbor. We propose a Neural Network named Guided Upsampling Network
which consists of a multiresolution architecture that jointly exploits
high-resolution and large context information. Then we introduce a new module
named Guided Upsampling Module (GUM) that enriches upsampling operators by
introducing a learnable transformation for semantic maps. It can be plugged
into any existing encoder-decoder architecture with little modifications and
low additional computation cost. We show with quantitative and qualitative
experiments how our network benefits from the use of GUM module. A
comprehensive set of experiments on the publicly available Cityscapes dataset
demonstrates that Guided Upsampling Network can efficiently process
high-resolution images in real-time while attaining state-of-the art
performances.
Conditional Random Fields as Recurrent Neural Networks for 3D Medical Imaging Segmentation
Authors: Miguel Monteiro, Mário A. T. Figueiredo, Arlindo L. Oliveira
Date: 2018-07-19 00:00:00
Tasks: 3D Medical Imaging Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation, Volumetric Medical Image Segmentation

The Conditional Random Field as a Recurrent Neural Network layer is a
recently proposed algorithm meant to be placed on top of an existing
Fully-Convolutional Neural Network to improve the quality of semantic
segmentation. In this paper, we test whether this algorithm, which was shown to
improve semantic segmentation for 2D RGB images, is able to improve
segmentation quality for 3D multi-modal medical images. We developed an
implementation of the algorithm which works for any number of spatial
dimensions, input/output image channels, and reference image channels. As far
as we know this is the first publicly available implementation of this sort. We
tested the algorithm with two distinct 3D medical imaging datasets, we
concluded that the performance differences observed were not statistically
significant. Finally, in the discussion section of the paper, we go into the
reasons as to why this technique transfers poorly from natural images to
medical images.
Can Artificial Intelligence Reliably Report Chest X-Rays?: Radiologist Validation of an Algorithm trained on 2.3 Million X-Rays
Authors: Preetham Putha, Manoj Tadepalli, Bhargava Reddy, Tarun Raj, Justy Antony Chiramal, Shalini Govil, Namita Sinha, Manjunath KS, Sundeep Reddivari, Ammar Jagirdar, Pooja Rao, Prashant Warier
Date: 2018-07-19 00:00:00
Tasks: Deep Learning, Diagnostic

Background: Chest X-rays are the most commonly performed, cost-effective diagnostic imaging tests ordered by physicians. A clinically validated AI system that can reliably separate normals from abnormals can be invaluble particularly in low-resource settings. The aim of this study was to develop and validate a deep learning system to detect various abnormalities seen on a chest X-ray. Methods: A deep learning system was trained on 2.3 million chest X-rays and their corresponding radiology reports to identify various abnormalities seen on a Chest X-ray. The system was tested against - 1. A three-radiologist majority on an independent, retrospectively collected set of 2000 X-rays(CQ2000) 2. Radiologist reports on a separate validation set of 100,000 scans(CQ100k). The primary accuracy measure was area under the ROC curve (AUC), estimated separately for each abnormality and for normal versus abnormal scans. Results: On the CQ2000 dataset, the deep learning system demonstrated an AUC of 0.92(CI 0.91-0.94) for detection of abnormal scans, and AUC(CI) of 0.96(0.94-0.98), 0.96(0.94-0.98), 0.95(0.87-1), 0.95(0.92-0.98), 0.93(0.90-0.96), 0.89(0.83-0.94), 0.91(0.87-0.96), 0.94(0.93-0.96), 0.98(0.97-1) for the detection of blunted costophrenic angle, cardiomegaly, cavity, consolidation, fibrosis, hilar enlargement, nodule, opacity and pleural effusion. The AUCs were similar on the larger CQ100k dataset except for detecting normals where the AUC was 0.86(0.85-0.86). Interpretation: Our study demonstrates that a deep learning algorithm trained on a large, well-labelled dataset can accurately detect multiple abnormalities on chest X-rays. As these systems improve in accuracy, applying deep learning to widen the reach of chest X-ray interpretation and improve reporting efficiency will add tremendous value in radiology workflows and public health screenings globally.
Selective Zero-Shot Classification with Augmented Attributes
Authors: Jie Song, Chengchao Shen, Jie Lei, An-Xiang Zeng, Kairi Ou, DaCheng Tao, Mingli Song
Date: 2018-07-19 00:00:00
Proceeding: ECCV 2018 9
Tasks: Attribute, Classification, General Classification, zero-shot-classification, Zero-Shot Learning

In this paper, we introduce a selective zero-shot classification problem: how
can the classifier avoid making dubious predictions? Existing attribute-based
zero-shot classification methods are shown to work poorly in the selective
classification scenario. We argue the under-complete human defined attribute
vocabulary accounts for the poor performance. We propose a selective zero-shot
classifier based on both the human defined and the automatically discovered
residual attributes. The proposed classifier is constructed by firstly learning
the defined and the residual attributes jointly. Then the predictions are
conducted within the subspace of the defined attributes. Finally, the
prediction confidence is measured by both the defined and the residual
attributes. Experiments conducted on several benchmarks demonstrate that our
classifier produces a superior performance to other methods under the
risk-coverage trade-off metric.
Semi-Dense 3D Reconstruction with a Stereo Event Camera
Authors: Yi Zhou, Guillermo Gallego, Henri Rebecq, Laurent Kneip, Hongdong Li, Davide Scaramuzza
Date: 2018-07-19 00:00:00
Proceeding: ECCV 2018 9
Tasks: 3D Reconstruction, Simultaneous Localization and Mapping

Event cameras are bio-inspired sensors that offer several advantages, such as
low latency, high-speed and high dynamic range, to tackle challenging scenarios
in computer vision. This paper presents a solution to the problem of 3D
reconstruction from data captured by a stereo event-camera rig moving in a
static scene, such as in the context of stereo Simultaneous Localization and
Mapping. The proposed method consists of the optimization of an energy function
designed to exploit small-baseline spatio-temporal consistency of events
triggered across both stereo image planes. To improve the density of the
reconstruction and to reduce the uncertainty of the estimation, a probabilistic
depth-fusion strategy is also developed. The resulting method has no special
requirements on either the motion of the stereo event-camera rig or on prior
knowledge about the scene. Experiments demonstrate our method can deal with
both texture-rich scenes as well as sparse scenes, outperforming
state-of-the-art stereo methods based on event data image representations.
Modeling Visual Context is Key to Augmenting Object Detection Datasets
Authors: Nikita Dvornik, Julien Mairal, Cordelia Schmid
Date: 2018-07-19 00:00:00
Proceeding: ECCV 2018 9
Tasks: Data Augmentation, object-detection, Object Detection

Performing data augmentation for learning deep neural networks is well known
to be important for training visual recognition systems. By artificially
increasing the number of training examples, it helps reducing overfitting and
improves generalization. For object detection, classical approaches for data
augmentation consist of generating images obtained by basic geometrical
transformations and color changes of original training images. In this work, we
go one step further and leverage segmentation annotations to increase the
number of object instances present on training data. For this approach to be
successful, we show that modeling appropriately the visual context surrounding
objects is crucial to place them in the right environment. Otherwise, we show
that the previous strategy actually hurts. With our context model, we achieve
significant mean average precision improvements when few labeled examples are
available on the VOC'12 benchmark.
Preventing Poisoning Attacks on AI based Threat Intelligence Systems
Authors: Nitika Khurana, Sudip Mittal, Anupam Joshi
Date: 2018-07-19 00:00:00

As AI systems become more ubiquitous, securing them becomes an emerging
challenge. Over the years, with the surge in online social media use and the
data available for analysis, AI systems have been built to extract, represent
and use this information. The credibility of this information extracted from
open sources, however, can often be questionable. Malicious or incorrect
information can cause a loss of money, reputation, and resources; and in
certain situations, pose a threat to human life. In this paper, we use an
ensembled semi-supervised approach to determine the credibility of Reddit posts
by estimating their reputation score to ensure the validity of information
ingested by AI systems. We demonstrate our approach in the cybersecurity
domain, where security analysts utilize these systems to determine possible
threats by analyzing the data scattered on social media websites, forums,
blogs, etc.
Image Reconstruction via Variational Network for Real-Time Hand-Held Sound-Speed Imaging
Authors: Valery Vishnevskiy, Sergio J Sanabria, Orcun Goksel
Date: 2018-07-19 00:00:00
Tasks: Image Reconstruction, Rolling Shutter Correction

Speed-of-sound is a biomechanical property for quantitative tissue
differentiation, with great potential as a new ultrasound-based image modality.
A conventional ultrasound array transducer can be used together with an
acoustic mirror, or so-called reflector, to reconstruct sound-speed images from
time-of-flight measurements to the reflector collected between transducer
element pairs, which constitutes a challenging problem of limited-angle
computed tomography. For this problem, we herein present a variational network
based image reconstruction architecture that is based on optimization loop
unrolling, and provide an efficient training protocol of this network
architecture on fully synthetic inclusion data. Our results indicate that the
learned model presents good generalization ability, being able to reconstruct
images with significantly different statistics compared to the training set.
Complex inclusion geometries were shown to be successfully reconstructed, also
improving over the prior-art by 23% in reconstruction error and by 10% in
contrast on synthetic data. In a phantom study, we demonstrated the detection
of multiple inclusions that were not distinguishable by prior-art
reconstruction, meanwhile improving the contrast by 27% for a stiff inclusion
and by 219% for a soft inclusion. Our reconstruction algorithm takes
approximately 10ms, enabling its use as a real-time imaging method on an
ultrasound machine, for which we are demonstrating an example preliminary setup
herein.
ISIC 2018-A Method for Lesion Segmentation
Authors: Hongdiao Wen, Rongjian Xu, Tie Zhang
Date: 2018-07-19 00:00:00
Tasks: Image Enhancement, Lesion Segmentation

Our team participate in the challenge of Task 1: Lesion Boundary Segmentation
, and use a combined network, one of which is designed by ourselves named
updcnn net and another is an improved VGG 16-layer net. Updcnn net uses reduced
size images for training, and VGG 16-layer net utilizes large size images.
Image enhancement is used to get a richer data set. We use boxes in the VGG
16-layer net network for local attention regularization to fine-tune the loss
function, which can increase the number of training data, and also make the
model more robust. In the test, the model is used for joint testing and
achieves good results.
Fuzzy quantification for linguistic data analysis and data mining
Authors: F. Díaz-Hermida, Juan. C. Vidal
Date: 2018-07-19 00:00:00
Tasks: Information Retrieval, Retrieval, Text Generation

Fuzzy quantification is a subtopic of fuzzy logic which deals with the
modelling of the quantified expressions we can find in natural language. Fuzzy
quantifiers have been successfully applied in several fields like fuzzy,
control, fuzzy databases, information retrieval, natural language generation,
etc. Their ability to model and evaluate linguistic expressions in a
mathematical way, makes fuzzy quantifiers very powerful for data analytics and
data mining applications. In this paper we will give a general overview of the
main applications of fuzzy quantifiers in this field as well as some ideas to
use them in new application contexts.
Revisiting Cross Modal Retrieval
Authors: Shah Nawaz, Muhammad Kamran Janjua, Alessandro Calefati, Ignazio Gallo
Date: 2018-07-19 00:00:00
Tasks: Cross-Modal Retrieval, Retrieval

This paper proposes a cross-modal retrieval system that leverages on image
and text encoding. Most multimodal architectures employ separate networks for
each modality to capture the semantic relationship between them. However, in
our work image-text encoding can achieve comparable results in terms of
cross-modal retrieval without having to use a separate network for each
modality. We show that text encodings can capture semantic relationships
between multiple modalities. In our knowledge, this work is the first of its
kind in terms of employing a single network and fused image-text embedding for
cross-modal retrieval. We evaluate our approach on two famous multimodal
datasets: MS-COCO and Flickr30K.
Speeding up the Hyperparameter Optimization of Deep Convolutional Neural Networks
Authors: Tobias Hinz, Nicolás Navarro-Guerrero, Sven Magg, Stefan Wermter
Date: 2018-07-19 00:00:00
Tasks: Hyperparameter Optimization, SMAC, SMAC+

Most learning algorithms require the practitioner to manually set the values
of many hyperparameters before the learning process can begin. However, with
modern algorithms, the evaluation of a given hyperparameter setting can take a
considerable amount of time and the search space is often very
high-dimensional. We suggest using a lower-dimensional representation of the
original data to quickly identify promising areas in the hyperparameter space.
This information can then be used to initialize the optimization algorithm for
the original, higher-dimensional data. We compare this approach with the
standard procedure of optimizing the hyperparameters only on the original
input.
  We perform experiments with various state-of-the-art hyperparameter
optimization algorithms such as random search, the tree of parzen estimators
(TPEs), sequential model-based algorithm configuration (SMAC), and a genetic
algorithm (GA). Our experiments indicate that it is possible to speed up the
optimization process by using lower-dimensional data representations at the
beginning, while increasing the dimensionality of the input later in the
optimization process. This is independent of the underlying optimization
procedure, making the approach promising for many existing hyperparameter
optimization algorithms.
Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks
Authors: Guotai Wang, Wenqi Li, Michael Aertsen, Jan Deprest, Sebastien Ourselin, Tom Vercauteren
Date: 2018-07-19 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Despite the state-of-the-art performance for medical image segmentation, deep
convolutional neural networks (CNNs) have rarely provided uncertainty
estimations regarding their segmentation outputs, e.g., model (epistemic) and
image-based (aleatoric) uncertainties. In this work, we analyze these different
types of uncertainties for CNN-based 2D and 3D medical image segmentation
tasks. We additionally propose a test-time augmentation-based aleatoric
uncertainty to analyze the effect of different transformations of the input
image on the segmentation output. Test-time augmentation has been previously
used to improve segmentation accuracy, yet not been formulated in a consistent
mathematical framework. Hence, we also propose a theoretical formulation of
test-time augmentation, where a distribution of the prediction is estimated by
Monte Carlo simulation with prior distributions of parameters in an image
acquisition model that involves image transformations and noise. We compare and
combine our proposed aleatoric uncertainty with model uncertainty. Experiments
with segmentation of fetal brains and brain tumors from 2D and 3D Magnetic
Resonance Images (MRI) showed that 1) the test-time augmentation-based
aleatoric uncertainty provides a better uncertainty estimation than calculating
the test-time dropout-based model uncertainty alone and helps to reduce
overconfident incorrect predictions, and 2) our test-time augmentation
outperforms a single-prediction baseline and dropout-based multiple
predictions.
Can We Assess Mental Health through Social Media and Smart Devices? Addressing Bias in Methodology and Evaluation
Authors: Adam Tsakalidis, Maria Liakata, Theo Damoulas, Alexandra I. Cristea
Date: 2018-07-19 00:00:00

Predicting mental health from smartphone and social media data on a
longitudinal basis has recently attracted great interest, with very promising
results being reported across many studies. Such approaches have the potential
to revolutionise mental health assessment, if their development and evaluation
follows a real world deployment setting. In this work we take a closer look at
state-of-the-art approaches, using different mental health datasets and
indicators, different feature sources and multiple simulations, in order to
assess their ability to generalise. We demonstrate that under a pragmatic
evaluation framework, none of the approaches deliver or even approach the
reported performances. In fact, we show that current state-of-the-art
approaches can barely outperform the most na\"ive baselines in the real-world
setting, posing serious questions not only about their deployment ability, but
also about the contribution of the derived features for the mental health
assessment task and how to make better use of such data in the future.
Generative Adversarial Networks for MR-CT Deformable Image Registration
Authors: Christine Tanner, Firat Ozdemir, Romy Profanter, Valeriy Vishnevsky, Ender Konukoglu, Orcun Goksel
Date: 2018-07-19 00:00:00
Tasks: Image Generation, Image Registration

Deformable Image Registration (DIR) of MR and CT images is one of the most
challenging registration task, due to the inherent structural differences of
the modalities and the missing dense ground truth. Recently cycle Generative
Adversarial Networks (cycle-GANs) have been used to learn the intensity
relationship between these 2 modalities for unpaired brain data. Yet its
usefulness for DIR was not assessed.
  In this study we evaluate the DIR performance for thoracic and abdominal
organs after synthesis by cycle-GAN. We show that geometric changes, which
differentiate the two populations (e.g. inhale vs. exhale), are readily
synthesized as well. This causes substantial problems for any application which
relies on spatial correspondences being preserved between the real and the
synthesized image (e.g. plan, segmentation, landmark propagation). To alleviate
this problem, we investigated reducing the spatial information provided to the
discriminator by decreasing the size of its receptive fields.
  Image synthesis was learned from 17 unpaired subjects per modality.
Registration performance was evaluated with respect to manual segmentations of
11 structures for 3 subjects from the VISERAL challenge. State-of-the-art DIR
methods based on Normalized Mutual Information (NMI), Modality Independent
Neighborhood Descriptor (MIND) and their novel combination achieved a mean
segmentation overlap ratio of 76.7, 67.7, 76.9%, respectively. This dropped to
69.1% or less when registering images synthesized by cycle-GAN based on local
correlation, due to the poor performance on the thoracic region, where large
lung volume changes were synthesized. Performance for the abdominal region was
similar to that of CT-MRI NMI registration (77.4 vs. 78.8%) when using 3D
synthesizing MRIs (12 slices) and medium sized receptive fields for the
discriminator.
Indexing Execution Patterns in Workflow Provenance Graphs through Generalized Trie Structures
Authors: Esteban García-Cuesta, José M. Gómez-Pérez
Date: 2018-07-19 00:00:00

Over the last years, scientific workflows have become mature enough to be
used in a production style. However, despite the increasing maturity, there is
still a shortage of tools for searching, adapting, and reusing workflows that
hinders a more generalized adoption by the scientific communities. Indeed, due
to the limited availability of machine-readable scientific metadata and the
heterogeneity of workflow specification formats and representations, new ways
to leverage alternative sources of information that complement existing
approaches are needed. In this paper we address such limitations by applying
statistically enriched generalized trie structures to exploit workflow
execution provenance information in order to assist the analysis, indexing and
search of scientific workflows. Our method bridges the gap between the
description of what a workflow is supposed to do according to its specification
and related metadata and what it actually does as recorded in its provenance
execution trace. In doing so, we also prove that the proposed method
outperforms SPARQL 1.1 Property Paths for querying provenance graphs.