1,133 research outputs found
Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs)
Positron emission tomography (PET) image synthesis plays an important role,
which can be used to boost the training data for computer aided diagnosis
systems. However, existing image synthesis methods have problems in
synthesizing the low resolution PET images. To address these limitations, we
propose multi-channel generative adversarial networks (M-GAN) based PET image
synthesis method. Different to the existing methods which rely on using
low-level features, the proposed M-GAN is capable to represent the features in
a high-level of semantic based on the adversarial learning concept. In
addition, M-GAN enables to take the input from the annotation (label) to
synthesize the high uptake regions e.g., tumors and from the computed
tomography (CT) images to constrain the appearance consistency and output the
synthetic PET images directly. Our results on 50 lung cancer PET-CT studies
indicate that our method was much closer to the real PET images when compared
with the existing methods.Comment: 9 pages, 2 figure
SAGE: Sequential Attribute Generator for Analyzing Glioblastomas using Limited Dataset
While deep learning approaches have shown remarkable performance in many
imaging tasks, most of these methods rely on availability of large quantities
of data. Medical image data, however, is scarce and fragmented. Generative
Adversarial Networks (GANs) have recently been very effective in handling such
datasets by generating more data. If the datasets are very small, however, GANs
cannot learn the data distribution properly, resulting in less diverse or
low-quality results. One such limited dataset is that for the concurrent gain
of 19 and 20 chromosomes (19/20 co-gain), a mutation with positive prognostic
value in Glioblastomas (GBM). In this paper, we detect imaging biomarkers for
the mutation to streamline the extensive and invasive prognosis pipeline. Since
this mutation is relatively rare, i.e. small dataset, we propose a novel
generative framework - the Sequential Attribute GEnerator (SAGE), that
generates detailed tumor imaging features while learning from a limited
dataset. Experiments show that not only does SAGE generate high quality tumors
when compared to standard Deep Convolutional GAN (DC-GAN) and Wasserstein GAN
with Gradient Penalty (WGAN-GP), it also captures the imaging biomarkers
accurately
Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters
Physicists at the Large Hadron Collider (LHC) rely on detailed simulations of
particle collisions to build expectations of what experimental data may look
like under different theory modeling assumptions. Petabytes of simulated data
are needed to develop analysis techniques, though they are expensive to
generate using existing algorithms and computing resources. The modeling of
detectors and the precise description of particle cascades as they interact
with the material in the calorimeter are the most computationally demanding
steps in the simulation pipeline. We therefore introduce a deep neural
network-based generative model to enable high-fidelity, fast, electromagnetic
calorimeter simulation. There are still challenges for achieving precision
across the entire phase space, but our current solution can reproduce a variety
of particle shower properties while achieving speed-up factors of up to
100,000. This opens the door to a new era of fast simulation that could
save significant computing time and disk space, while extending the reach of
physics searches and precision measurements at the LHC and beyond.Comment: 6 pages, 3 figures; version accepted by Physical Review Letters (PRL
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