pwc-archive/papers-with-abstracts huggingface dataset:
SUSAN: Segment Unannotated image Structure using Adversarial Network
Authors: Fang Liu
Date: 2018-12-03 00:00:00
Tasks: Generative Adversarial Network, Image Segmentation, Image-to-Image Translation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Segmentation of magnetic resonance (MR) images is a fundamental step in many
medical imaging-based applications. The recent implementation of deep
convolutional neural networks (CNNs) in image processing has been shown to have
significant impacts on medical image segmentation. Network training of
segmentation CNNs typically requires images and paired annotation data
representing pixel-wise tissue labels referred to as masks. However, the
supervised training of highly efficient CNNs with deeper structure and more
network parameters requires a large number of training images and paired tissue
masks. Thus, there is great need to develop a generalized CNN-based
segmentation method which would be applicable for a wide variety of MR image
datasets with different tissue contrasts. The purpose of this study was to
develop and evaluate a generalized CNN-based method for fully-automated
segmentation of different MR image datasets using a single set of annotated
training data. A technique called cycle-consistent generative adversarial
network (CycleGAN) is applied as the core of the proposed method to perform
image-to-image translation between MR image datasets with different tissue
contrasts. A joint segmentation network is incorporated into the adversarial
network to obtain additional segmentation functionality. The proposed method
was evaluated for segmenting bone and cartilage on two clinical knee MR image
datasets acquired at our institution using only a single set of annotated data
from a publicly available knee MR image dataset. The new technique may further
improve the applicability and efficiency of CNN-based segmentation of medical
images while eliminating the need for large amounts of annotated training data.
Modeling Treatment Delays for Patients using Feature Label Pairs in a Time Series
Authors: Weiyu Huang, Yunlong Wang, Li Zhou, Emily Zhao, Yilian Yuan, Alejandro Ribero
Date: 2018-12-03 00:00:00
Tasks: Marketing, Time Series, Time Series Analysis

Pharmaceutical targeting is one of key inputs for making sales and marketing
strategy planning. Targeting list is built on predicting physician's sales
potential of certain type of patient. In this paper, we present a
time-sensitive targeting framework leveraging time series model to predict
patient's disease and treatment progression. We create time features by
extracting service history within a certain period, and record whether the
event happens in a look-forward period. Such feature-label pairs are examined
across all time periods and all patients to train a model. It keeps the
inherent order of services and evaluates features associated to the imminent
future, which contribute to improved accuracy.
Universal Perturbation Attack Against Image Retrieval
Authors: Jie Li, Rongrong Ji, Hong Liu, Xiaopeng Hong, Yue Gao, Qi Tian
Date: 2018-12-03 00:00:00
Proceeding: ICCV 2019 10
Tasks: image-classification, Image Classification, Image Retrieval, Retrieval

Universal adversarial perturbations (UAPs), a.k.a. input-agnostic perturbations, has been proved to exist and be able to fool cutting-edge deep learning models on most of the data samples. Existing UAP methods mainly focus on attacking image classification models. Nevertheless, little attention has been paid to attacking image retrieval systems. In this paper, we make the first attempt in attacking image retrieval systems. Concretely, image retrieval attack is to make the retrieval system return irrelevant images to the query at the top ranking list. It plays an important role to corrupt the neighbourhood relationships among features in image retrieval attack. To this end, we propose a novel method to generate retrieval-against UAP to break the neighbourhood relationships of image features via degrading the corresponding ranking metric. To expand the attack method to scenarios with varying input sizes or untouchable network parameters, a multi-scale random resizing scheme and a ranking distillation strategy are proposed. We evaluate the proposed method on four widely-used image retrieval datasets, and report a significant performance drop in terms of different metrics, such as mAP and mP@10. Finally, we test our attack methods on the real-world visual search engine, i.e., Google Images, which demonstrates the practical potentials of our methods.
XNet: A convolutional neural network (CNN) implementation for medical X-Ray image segmentation suitable for small datasets
Authors: Joseph Bullock, Carolina Cuesta-Lazaro, Arnau Quera-Bofarull
Date: 2018-12-03 00:00:00
Tasks: Clustering, Image Enhancement, Image Segmentation, Medical X-Ray Image Segmentation, Segmentation, Semantic Segmentation

X-Ray image enhancement, along with many other medical image processing
applications, requires the segmentation of images into bone, soft tissue, and
open beam regions. We apply a machine learning approach to this problem,
presenting an end-to-end solution which results in robust and efficient
inference. Since medical institutions frequently do not have the resources to
process and label the large quantity of X-Ray images usually needed for neural
network training, we design an end-to-end solution for small datasets, while
achieving state-of-the-art results. Our implementation produces an overall
accuracy of 92%, F1 score of 0.92, and an AUC of 0.98, surpassing classical
image processing techniques, such as clustering and entropy based methods,
while improving upon the output of existing neural networks used for
segmentation in non-medical contexts. The code used for this project is
available online.
Semi-supervised Rare Disease Detection Using Generative Adversarial Network
Authors: Wenyuan Li, Yunlong Wang, Yong Cai, Corey Arnold, Emily Zhao, Yilian Yuan
Date: 2018-12-03 00:00:00
Tasks: Generative Adversarial Network

Rare diseases affect a relatively small number of people, which limits
investment in research for treatments and cures. Developing an efficient method
for rare disease detection is a crucial first step towards subsequent clinical
research. In this paper, we present a semi-supervised learning framework for
rare disease detection using generative adversarial networks. Our method takes
advantage of the large amount of unlabeled data for disease detection and
achieves the best results in terms of precision-recall score compared to
baseline techniques.
Few-Shot Self Reminder to Overcome Catastrophic Forgetting
Authors: Junfeng Wen, Yanshuai Cao, Ruitong Huang
Date: 2018-12-03 00:00:00
Tasks: Continual Learning

Deep neural networks are known to suffer the catastrophic forgetting problem,
where they tend to forget the knowledge from the previous tasks when
sequentially learning new tasks. Such failure hinders the application of deep
learning based vision system in continual learning settings. In this work, we
present a simple yet surprisingly effective way of preventing catastrophic
forgetting. Our method, called Few-shot Self Reminder (FSR), regularizes the
neural net from changing its learned behaviour by performing logit matching on
selected samples kept in episodic memory from the old tasks. Surprisingly, this
simplistic approach only requires to retrain a small amount of data in order to
outperform previous methods in knowledge retention. We demonstrate the
superiority of our method to the previous ones in two different continual
learning settings on popular benchmarks, as well as a new continual learning
problem where tasks are designed to be more dissimilar.
Towards Theoretical Understanding of Large Batch Training in Stochastic Gradient Descent
Authors: Xiaowu Dai, Yuhua Zhu
Date: 2018-12-03 00:00:00

Stochastic gradient descent (SGD) is almost ubiquitously used for training
non-convex optimization tasks. Recently, a hypothesis proposed by Keskar et al.
[2017] that large batch methods tend to converge to sharp minimizers has
received increasing attention. We theoretically justify this hypothesis by
providing new properties of SGD in both finite-time and asymptotic regimes. In
particular, we give an explicit escaping time of SGD from a local minimum in
the finite-time regime and prove that SGD tends to converge to flatter minima
in the asymptotic regime (although may take exponential time to converge)
regardless of the batch size. We also find that SGD with a larger ratio of
learning rate to batch size tends to converge to a flat minimum faster,
however, its generalization performance could be worse than the SGD with a
smaller ratio of learning rate to batch size. We include numerical experiments
to corroborate these theoretical findings.
Exploiting Wireless Channel State Information Structures Beyond Linear Correlations: A Deep Learning Approach
Authors: Zhiyuan Jiang, Sheng Chen, Andreas F. Molisch, Rath Vannithamby, Sheng Zhou, Zhisheng Niu
Date: 2018-12-03 00:00:00
Tasks: Dimensionality Reduction

Knowledge of information about the propagation channel in which a wireless
system operates enables better, more efficient approaches for signal
transmissions. Therefore, channel state information (CSI) plays a pivotal role
in the system performance. The importance of CSI is in fact growing in the
upcoming 5G and beyond systems, e.g., for the implementation of massive
multiple-input multiple-output (MIMO). However, the acquisition of timely and
accurate CSI has long been considered as a major issue, and becomes
increasingly challenging due to the need for obtaining CSI of many antenna
elements in massive MIMO systems. To cope with this challenge, existing works
mainly focus on exploiting linear structures of CSI, such as CSI correlations
in the spatial domain, to achieve dimensionality reduction. In this article, we
first systematically review the state-of-the-art on CSI structure exploitation;
then extend to seek for deeper structures that enable remote CSI inference
wherein a data-driven deep neural network (DNN) approach is necessary due to
model inadequacy. We develop specific DNN designs suitable for CSI data. Case
studies are provided to demonstrate great potential in this direction for
future performance enhancement.
Interpretable Clustering via Optimal Trees
Authors: Dimitris Bertsimas, Agni Orfanoudaki, Holly Wiberg
Date: 2018-12-03 00:00:00
Tasks: Clustering

State-of-the-art clustering algorithms use heuristics to partition the
feature space and provide little insight into the rationale for cluster
membership, limiting their interpretability. In healthcare applications, the
latter poses a barrier to the adoption of these methods since medical
researchers are required to provide detailed explanations of their decisions in
order to gain patient trust and limit liability. We present a new unsupervised
learning algorithm that leverages Mixed Integer Optimization techniques to
generate interpretable tree-based clustering models. Utilizing the flexible
framework of Optimal Trees, our method approximates the globally optimal
solution leading to high quality partitions of the feature space. Our
algorithm, can incorporate various internal validation metrics, naturally
determines the optimal number of clusters, and is able to account for mixed
numeric and categorical data. It achieves comparable or superior performance on
both synthetic and real world datasets when compared to K-Means while offering
significantly higher interpretability.
Large Spectral Density Matrix Estimation by Thresholding
Authors: Yiming Sun, Yige Li, Amy Kuceyeski, Sumanta Basu
Date: 2018-12-03 00:00:00
Tasks: Functional Connectivity, Time Series, Time Series Analysis

Spectral density matrix estimation of multivariate time series is a classical
problem in time series and signal processing. In modern neuroscience, spectral
density based metrics are commonly used for analyzing functional connectivity
among brain regions. In this paper, we develop a non-asymptotic theory for
regularized estimation of high-dimensional spectral density matrices of
Gaussian and linear processes using thresholded versions of averaged
periodograms. Our theoretical analysis ensures that consistent estimation of
spectral density matrix of a $p$-dimensional time series using $n$ samples is
possible under high-dimensional regime $\log p / n \rightarrow 0$ as long as
the true spectral density is approximately sparse. A key technical component of
our analysis is a new concentration inequality of average periodogram around
its expectation, which is of independent interest. Our estimation consistency
results complement existing results for shrinkage based estimators of
multivariate spectral density, which require no assumption on sparsity but only
ensure consistent estimation in a regime $p^2/n \rightarrow 0$. In addition,
our proposed thresholding based estimators perform consistent and automatic
edge selection when learning coherence networks among the components of a
multivariate time series. We demonstrate the advantage of our estimators using
simulation studies and a real data application on functional connectivity
analysis with fMRI data.
Modeling Irregularly Sampled Clinical Time Series
Authors: Satya Narayan Shukla, Benjamin M. Marlin
Date: 2018-12-03 00:00:00
Tasks: Deep Learning, Length-of-Stay prediction, Prediction, Time Series, Time Series Analysis

While the volume of electronic health records (EHR) data continues to grow,
it remains rare for hospital systems to capture dense physiological data
streams, even in the data-rich intensive care unit setting. Instead, typical
EHR records consist of sparse and irregularly observed multivariate time
series, which are well understood to present particularly challenging problems
for machine learning methods. In this paper, we present a new deep learning
architecture for addressing this problem based on the use of a semi-parametric
interpolation network followed by the application of a prediction network. The
interpolation network allows for information to be shared across multiple
dimensions during the interpolation stage, while any standard deep learning
model can be used for the prediction network. We investigate the performance of
this architecture on the problems of mortality and length of stay prediction.
Modeling disease progression in longitudinal EHR data using continuous-time hidden Markov models
Authors: Aman Verma, Guido Powell, Yu Luo, David Stephens, David L. Buckeridge
Date: 2018-12-03 00:00:00

Modeling disease progression in healthcare administrative databases is
complicated by the fact that patients are observed only at irregular intervals
when they seek healthcare services. In a longitudinal cohort of 76,888 patients
with chronic obstructive pulmonary disease (COPD), we used a continuous-time
hidden Markov model with a generalized linear model to model healthcare
utilization events. We found that the fitted model provides interpretable
results suitable for summarization and hypothesis generation.
Automated Segmentation of Cervical Nuclei in Pap Smear Images using Deformable Multi-path Ensemble Model
Authors: Jie Zhao, Quanzheng Li, Xiang Li, Hongfeng Li, Li Zhang
Date: 2018-12-03 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Pap smear testing has been widely used for detecting cervical cancers based on the morphology properties of cell nuclei in microscopic image. An accurate nuclei segmentation could thus improve the success rate of cervical cancer screening. In this work, a method of automated cervical nuclei segmentation using Deformable Multipath Ensemble Model (D-MEM) is proposed. The approach adopts a U-shaped convolutional network as a backbone network, in which dense blocks are used to transfer feature information more effectively. To increase the flexibility of the model, we then use deformable convolution to deal with different nuclei irregular shapes and sizes. To reduce the predictive bias, we further construct multiple networks with different settings, which form an ensemble model. The proposed segmentation framework has achieved state-of-the-art accuracy on Herlev dataset with Zijdenbos similarity index (ZSI) of 0.933, and has the potential to be extended for solving other medical image segmentation tasks.
Generalization in anti-causal learning
Authors: Niki Kilbertus, Giambattista Parascandolo, Bernhard Schölkopf
Date: 2018-12-03 00:00:00
Tasks: BIG-bench Machine Learning

The ability to learn and act in novel situations is still a prerogative of
animate intelligence, as current machine learning methods mostly fail when
moving beyond the standard i.i.d. setting. What is the reason for this
discrepancy? Most machine learning tasks are anti-causal, i.e., we infer causes
(labels) from effects (observations). Typically, in supervised learning we
build systems that try to directly invert causal mechanisms. Instead, in this
paper we argue that strong generalization capabilities crucially hinge on
searching and validating meaningful hypotheses, requiring access to a causal
model. In such a framework, we want to find a cause that leads to the observed
effect. Anti-causal models are used to drive this search, but a causal model is
required for validation. We investigate the fundamental differences between
causal and anti-causal tasks, discuss implications for topics ranging from
adversarial attacks to disentangling factors of variation, and provide
extensive evidence from the literature to substantiate our view. We advocate
for incorporating causal models in supervised learning to shift the paradigm
from inference only, to search and validation.
Elastic Boundary Projection for 3D Medical Image Segmentation
Authors: Tianwei Ni, Lingxi Xie, Huangjie Zheng, Elliot K. Fishman, Alan L. Yuille
Date: 2018-12-03 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Medical Imaging Segmentation, Image Segmentation, Medical Image Analysis, Medical Image Segmentation, Organ Segmentation, Segmentation, Semantic Segmentation

We focus on an important yet challenging problem: using a 2D deep network to deal with 3D segmentation for medical image analysis. Existing approaches either applied multi-view planar (2D) networks or directly used volumetric (3D) networks for this purpose, but both of them are not ideal: 2D networks cannot capture 3D contexts effectively, and 3D networks are both memory-consuming and less stable arguably due to the lack of pre-trained models. In this paper, we bridge the gap between 2D and 3D using a novel approach named Elastic Boundary Projection (EBP). The key observation is that, although the object is a 3D volume, what we really need in segmentation is to find its boundary which is a 2D surface. Therefore, we place a number of pivot points in the 3D space, and for each pivot, we determine its distance to the object boundary along a dense set of directions. This creates an elastic shell around each pivot which is initialized as a perfect sphere. We train a 2D deep network to determine whether each ending point falls within the object, and gradually adjust the shell so that it gradually converges to the actual shape of the boundary and thus achieves the goal of segmentation. EBP allows boundary-based segmentation without cutting a 3D volume into slices or patches, which stands out from conventional 2D and 3D approaches. EBP achieves promising accuracy in abdominal organ segmentation. Our code has been open-sourced https://github.com/twni2016/Elastic-Boundary-Projection.
JSR-Net: A Deep Network for Joint Spatial-Radon Domain CT Reconstruction from incomplete data
Authors: Haimiao Zhang, Bin Dong, Baodong Liu
Date: 2018-12-03 00:00:00
Tasks: CT Reconstruction, Image Reconstruction

CT image reconstruction from incomplete data, such as sparse views and
limited angle reconstruction, is an important and challenging problem in
medical imaging. This work proposes a new deep convolutional neural network
(CNN), called JSR-Net, that jointly reconstructs CT images and their associated
Radon domain projections. JSR-Net combines the traditional model-based approach
with deep architecture design of deep learning. A hybrid loss function is
adapted to improve the performance of the JSR-Net making it more effective in
protecting important image structures. Numerical experiments demonstrate that
JSR-Net outperforms some latest model-based reconstruction methods, as well as
a recently proposed deep model.
Knowledge-driven generative subspaces for modeling multi-view dependencies in medical data
Authors: Parvathy Sudhir Pillai, Tze-Yun Leong
Date: 2018-12-03 00:00:00

Early detection of Alzheimer's disease (AD) and identification of potential
risk/beneficial factors are important for planning and administering timely
interventions or preventive measures. In this paper, we learn a disease model
for AD that combines genotypic and phenotypic profiles, and cognitive health
metrics of patients. We propose a probabilistic generative subspace that
describes the correlative, complementary and domain-specific semantics of the
dependencies in multi-view, multi-modality medical data. Guided by domain
knowledge and using the latent consensus between abstractions of multi-view
data, we model the fusion as a data generating process. We show that our
approach can potentially lead to i) explainable clinical predictions and ii)
improved AD diagnoses.
Prediction of New Onset Diabetes after Liver Transplant
Authors: Angeline Yasodhara, Mamatha Bhat, Anna Goldenberg
Date: 2018-12-03 00:00:00
Tasks: Prediction, Time-to-Event Prediction

25% of people who received a liver transplant will go on to develop diabetes within the next 5 years. These thousands of individuals are at 2-fold higher risk of cardiovascular events, graft loss, infections, as well as lower long-term survival. This is partly due to the medication used during and/or after transplant that significantly impacts metabolic balance. To assess which medication best suits the patient's condition, clinicians need an accurate estimate of diabetes risk. Both patient's historical data and observations at the current visit are informative in predicting whether the patient will develop diabetes within the following year. In this work we compared a variety of time-to-event prediction models as well as classifiers predicting the likelihood of the event within a year from the current checkup. We are particularly interested in comparing two types of models: 1) standard time-to-event predictors where the historical measurements are merely concatenated, 2) incorporating Deep Markov Model to first obtain low-dimensional embedding of historical data and then using this embedding as an additional input into the model. We compared a variety of algorithms including standard and regularized Cox proportional-hazards model (CPH), mixed effect random forests, survival-forests and Weibull Time-To-Event Recurrent Neural Network (WTTE-RNN). The results show that although all methods' performances varied from year to year and there was no clear winner across all the time points, regularized CPH model that used 1 to 3 years of historical visits data on average achieved a high, clinically relevant Concordance Index of .863. We thus recommend this model for further prospective clinical validation and hopefully, an eventual use in the clinic to improve clinicians' ability to personalize post-operative care and reduce the incidence of new-onset diabetes post liver transplant.