1,116 research outputs found
CAS-CNN: A Deep Convolutional Neural Network for Image Compression Artifact Suppression
Lossy image compression algorithms are pervasively used to reduce the size of
images transmitted over the web and recorded on data storage media. However, we
pay for their high compression rate with visual artifacts degrading the user
experience. Deep convolutional neural networks have become a widespread tool to
address high-level computer vision tasks very successfully. Recently, they have
found their way into the areas of low-level computer vision and image
processing to solve regression problems mostly with relatively shallow
networks.
We present a novel 12-layer deep convolutional network for image compression
artifact suppression with hierarchical skip connections and a multi-scale loss
function. We achieve a boost of up to 1.79 dB in PSNR over ordinary JPEG and an
improvement of up to 0.36 dB over the best previous ConvNet result. We show
that a network trained for a specific quality factor (QF) is resilient to the
QF used to compress the input image - a single network trained for QF 60
provides a PSNR gain of more than 1.5 dB over the wide QF range from 40 to 76.Comment: 8 page
Prospects for Theranostics in Neurosurgical Imaging: Empowering Confocal Laser Endomicroscopy Diagnostics via Deep Learning
Confocal laser endomicroscopy (CLE) is an advanced optical fluorescence
imaging technology that has the potential to increase intraoperative precision,
extend resection, and tailor surgery for malignant invasive brain tumors
because of its subcellular dimension resolution. Despite its promising
diagnostic potential, interpreting the gray tone fluorescence images can be
difficult for untrained users. In this review, we provide a detailed
description of bioinformatical analysis methodology of CLE images that begins
to assist the neurosurgeon and pathologist to rapidly connect on-the-fly
intraoperative imaging, pathology, and surgical observation into a
conclusionary system within the concept of theranostics. We present an overview
and discuss deep learning models for automatic detection of the diagnostic CLE
images and discuss various training regimes and ensemble modeling effect on the
power of deep learning predictive models. Two major approaches reviewed in this
paper include the models that can automatically classify CLE images into
diagnostic/nondiagnostic, glioma/nonglioma, tumor/injury/normal categories and
models that can localize histological features on the CLE images using weakly
supervised methods. We also briefly review advances in the deep learning
approaches used for CLE image analysis in other organs. Significant advances in
speed and precision of automated diagnostic frame selection would augment the
diagnostic potential of CLE, improve operative workflow and integration into
brain tumor surgery. Such technology and bioinformatics analytics lend
themselves to improved precision, personalization, and theranostics in brain
tumor treatment.Comment: See the final version published in Frontiers in Oncology here:
https://www.frontiersin.org/articles/10.3389/fonc.2018.00240/ful
Predicting Landslides Using Locally Aligned Convolutional Neural Networks
Landslides, movement of soil and rock under the influence of gravity, are
common phenomena that cause significant human and economic losses every year.
Experts use heterogeneous features such as slope, elevation, land cover,
lithology, rock age, and rock family to predict landslides. To work with such
features, we adapted convolutional neural networks to consider relative spatial
information for the prediction task. Traditional filters in these networks
either have a fixed orientation or are rotationally invariant. Intuitively, the
filters should orient uphill, but there is not enough data to learn the concept
of uphill; instead, it can be provided as prior knowledge. We propose a model
called Locally Aligned Convolutional Neural Network, LACNN, that follows the
ground surface at multiple scales to predict possible landslide occurrence for
a single point. To validate our method, we created a standardized dataset of
georeferenced images consisting of the heterogeneous features as inputs, and
compared our method to several baselines, including linear regression, a neural
network, and a convolutional network, using log-likelihood error and Receiver
Operating Characteristic curves on the test set. Our model achieves 2-7%
improvement in terms of accuracy and 2-15% boost in terms of log likelihood
compared to the other proposed baselines.Comment: Published in IJCAI 202
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