7,782 research outputs found
Semi-supervised Task-driven Data Augmentation for Medical Image Segmentation
Supervised learning-based segmentation methods typically require a large
number of annotated training data to generalize well at test time. In medical
applications, curating such datasets is not a favourable option because
acquiring a large number of annotated samples from experts is time-consuming
and expensive. Consequently, numerous methods have been proposed in the
literature for learning with limited annotated examples. Unfortunately, the
proposed approaches in the literature have not yet yielded significant gains
over random data augmentation for image segmentation, where random
augmentations themselves do not yield high accuracy. In this work, we propose a
novel task-driven data augmentation method for learning with limited labeled
data where the synthetic data generator, is optimized for the segmentation
task. The generator of the proposed method models intensity and shape
variations using two sets of transformations, as additive intensity
transformations and deformation fields. Both transformations are optimized
using labeled as well as unlabeled examples in a semi-supervised framework. Our
experiments on three medical datasets, namely cardic, prostate and pancreas,
show that the proposed approach significantly outperforms standard augmentation
and semi-supervised approaches for image segmentation in the limited annotation
setting. The code is made publicly available at
https://github.com/krishnabits001/taskdrivendataaugmentation.Comment: 15 pages, 11 Figures, 3 tables. Accepted at Medical Image Analysis,
202
DADA: Deep Adversarial Data Augmentation for Extremely Low Data Regime Classification
Deep learning has revolutionized the performance of classification, but
meanwhile demands sufficient labeled data for training. Given insufficient
data, while many techniques have been developed to help combat overfitting, the
challenge remains if one tries to train deep networks, especially in the
ill-posed extremely low data regimes: only a small set of labeled data are
available, and nothing -- including unlabeled data -- else. Such regimes arise
from practical situations where not only data labeling but also data collection
itself is expensive. We propose a deep adversarial data augmentation (DADA)
technique to address the problem, in which we elaborately formulate data
augmentation as a problem of training a class-conditional and supervised
generative adversarial network (GAN). Specifically, a new discriminator loss is
proposed to fit the goal of data augmentation, through which both real and
augmented samples are enforced to contribute to and be consistent in finding
the decision boundaries. Tailored training techniques are developed
accordingly. To quantitatively validate its effectiveness, we first perform
extensive simulations to show that DADA substantially outperforms both
traditional data augmentation and a few GAN-based options. We then extend
experiments to three real-world small labeled datasets where existing data
augmentation and/or transfer learning strategies are either less effective or
infeasible. All results endorse the superior capability of DADA in enhancing
the generalization ability of deep networks trained in practical extremely low
data regimes. Source code is available at
https://github.com/SchafferZhang/DADA.Comment: 15 pages, 5 figure
Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records
The rapid growth of Electronic Health Records (EHRs), as well as the
accompanied opportunities in Data-Driven Healthcare (DDH), has been attracting
widespread interests and attentions. Recent progress in the design and
applications of deep learning methods has shown promising results and is
forcing massive changes in healthcare academia and industry, but most of these
methods rely on massive labeled data. In this work, we propose a general deep
learning framework which is able to boost risk prediction performance with
limited EHR data. Our model takes a modified generative adversarial network
namely ehrGAN, which can provide plausible labeled EHR data by mimicking real
patient records, to augment the training dataset in a semi-supervised learning
manner. We use this generative model together with a convolutional neural
network (CNN) based prediction model to improve the onset prediction
performance. Experiments on two real healthcare datasets demonstrate that our
proposed framework produces realistic data samples and achieves significant
improvements on classification tasks with the generated data over several
stat-of-the-art baselines.Comment: To appear in ICDM 2017. This is the full version of paper with 8
page
Yes, we GAN: Applying Adversarial Techniques for Autonomous Driving
Generative Adversarial Networks (GAN) have gained a lot of popularity from
their introduction in 2014 till present. Research on GAN is rapidly growing and
there are many variants of the original GAN focusing on various aspects of deep
learning. GAN are perceived as the most impactful direction of machine learning
in the last decade. This paper focuses on the application of GAN in autonomous
driving including topics such as advanced data augmentation, loss function
learning, semi-supervised learning, etc. We formalize and review key
applications of adversarial techniques and discuss challenges and open problems
to be addressed.Comment: Accepted for publication in Electronic Imaging, Autonomous Vehicles
and Machines 2019. arXiv admin note: text overlap with arXiv:1606.05908 by
other author
Learning Discrete Representations via Information Maximizing Self-Augmented Training
Learning discrete representations of data is a central machine learning task
because of the compactness of the representations and ease of interpretation.
The task includes clustering and hash learning as special cases. Deep neural
networks are promising to be used because they can model the non-linearity of
data and scale to large datasets. However, their model complexity is huge, and
therefore, we need to carefully regularize the networks in order to learn
useful representations that exhibit intended invariance for applications of
interest. To this end, we propose a method called Information Maximizing
Self-Augmented Training (IMSAT). In IMSAT, we use data augmentation to impose
the invariance on discrete representations. More specifically, we encourage the
predicted representations of augmented data points to be close to those of the
original data points in an end-to-end fashion. At the same time, we maximize
the information-theoretic dependency between data and their predicted discrete
representations. Extensive experiments on benchmark datasets show that IMSAT
produces state-of-the-art results for both clustering and unsupervised hash
learning.Comment: To appear at ICML 201
Data augmentation using learned transformations for one-shot medical image segmentation
Image segmentation is an important task in many medical applications. Methods
based on convolutional neural networks attain state-of-the-art accuracy;
however, they typically rely on supervised training with large labeled
datasets. Labeling medical images requires significant expertise and time, and
typical hand-tuned approaches for data augmentation fail to capture the complex
variations in such images.
We present an automated data augmentation method for synthesizing labeled
medical images. We demonstrate our method on the task of segmenting magnetic
resonance imaging (MRI) brain scans. Our method requires only a single
segmented scan, and leverages other unlabeled scans in a semi-supervised
approach. We learn a model of transformations from the images, and use the
model along with the labeled example to synthesize additional labeled examples.
Each transformation is comprised of a spatial deformation field and an
intensity change, enabling the synthesis of complex effects such as variations
in anatomy and image acquisition procedures. We show that training a supervised
segmenter with these new examples provides significant improvements over
state-of-the-art methods for one-shot biomedical image segmentation. Our code
is available at https://github.com/xamyzhao/brainstorm.Comment: 9 pages, CVPR 201
Power Pooling Operators and Confidence Learning for Semi-Supervised Sound Event Detection
In recent years, the involvement of synthetic strongly labeled data,weakly
labeled data and unlabeled data has drawn much research attentionin
semi-supervised sound event detection (SSED). Self-training models carry out
predictions without strong annotations and then take predictions with high
probabilities as pseudo-labels for retraining. Such models have shown its
effectiveness in SSED. However, probabilities are poorly calibrated confidence
estimates, and samples with low probabilities are ignored. Hence, we introduce
a method of learning confidence deliberately and retaining all data distinctly
by applying confidence as weights. Additionally, linear pooling has been
considered as a state-of-the-art aggregation function for SSED with weak
labeling. In this paper, we propose a power pooling function whose coefficient
can be trained automatically to achieve nonlinearity. A confidencebased
semi-supervised sound event detection (C-SSED) framework is designed to combine
confidence and power pooling. The experimental results demonstrate that
confidence is proportional to the accuracy of the predictions. The power
pooling function outperforms linear pooling at both error rate and F1 results.
In addition, the C-SSED framework achieves a relative error rate reduction of
34% in contrast to the baseline model
Generative Adversarial Network in Medical Imaging: A Review
Generative adversarial networks have gained a lot of attention in the
computer vision community due to their capability of data generation without
explicitly modelling the probability density function. The adversarial loss
brought by the discriminator provides a clever way of incorporating unlabeled
samples into training and imposing higher order consistency. This has proven to
be useful in many cases, such as domain adaptation, data augmentation, and
image-to-image translation. These properties have attracted researchers in the
medical imaging community, and we have seen rapid adoption in many traditional
and novel applications, such as image reconstruction, segmentation, detection,
classification, and cross-modality synthesis. Based on our observations, this
trend will continue and we therefore conducted a review of recent advances in
medical imaging using the adversarial training scheme with the hope of
benefiting researchers interested in this technique.Comment: 24 pages; v4; added missing references from before Jan 1st 2019;
accepted to MedI
Unsupervised Paraphrasing without Translation
Paraphrasing exemplifies the ability to abstract semantic content from
surface forms. Recent work on automatic paraphrasing is dominated by methods
leveraging Machine Translation (MT) as an intermediate step. This contrasts
with humans, who can paraphrase without being bilingual. This work proposes to
learn paraphrasing models from an unlabeled monolingual corpus only. To that
end, we propose a residual variant of vector-quantized variational
auto-encoder.
We compare with MT-based approaches on paraphrase identification, generation,
and training augmentation. Monolingual paraphrasing outperforms unsupervised
translation in all settings. Comparisons with supervised translation are more
mixed: monolingual paraphrasing is interesting for identification and
augmentation; supervised translation is superior for generation.Comment: ACL 201
Implicit Pairs for Boosting Unpaired Image-to-Image Translation
In image-to-image translation the goal is to learn a mapping from one image
domain to another. In the case of supervised approaches the mapping is learned
from paired samples. However, collecting large sets of image pairs is often
either prohibitively expensive or not possible. As a result, in recent years
more attention has been given to techniques that learn the mapping from
unpaired sets.
In our work, we show that injecting implicit pairs into unpaired sets
strengthens the mapping between the two domains, improves the compatibility of
their distributions, and leads to performance boosting of unsupervised
techniques by over 14% across several measurements.
The competence of the implicit pairs is further displayed with the use of
pseudo-pairs, i.e., paired samples which only approximate a real pair. We
demonstrate the effect of the approximated implicit samples on image-to-image
translation problems, where such pseudo-pairs may be synthesized in one
direction, but not in the other. We further show that pseudo-pairs are
significantly more effective as implicit pairs in an unpaired setting, than
directly using them explicitly in a paired setting
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