15,804 research outputs found
Brain Tumor Segmentation Based on Refined Fully Convolutional Neural Networks with A Hierarchical Dice Loss
As a basic task in computer vision, semantic segmentation can provide
fundamental information for object detection and instance segmentation to help
the artificial intelligence better understand real world. Since the proposal of
fully convolutional neural network (FCNN), it has been widely used in semantic
segmentation because of its high accuracy of pixel-wise classification as well
as high precision of localization. In this paper, we apply several famous FCNN
to brain tumor segmentation, making comparisons and adjusting network
architectures to achieve better performance measured by metrics such as
precision, recall, mean of intersection of union (mIoU) and dice score
coefficient (DSC). The adjustments to the classic FCNN include adding more
connections between convolutional layers, enlarging decoders after up sample
layers and changing the way shallower layers' information is reused. Besides
the structure modification, we also propose a new classifier with a
hierarchical dice loss. Inspired by the containing relationship between
classes, the loss function converts multiple classification to multiple binary
classification in order to counteract the negative effect caused by imbalance
data set. Massive experiments have been done on the training set and testing
set in order to assess our refined fully convolutional neural networks and new
types of loss function. Competitive figures prove they are more effective than
their predecessors.Comment: 14 pages, 7 figures, 6 table
Reducing the Model Variance of a Rectal Cancer Segmentation Network
In preoperative imaging, the demarcation of rectal cancer with magnetic
resonance images provides an important basis for cancer staging and treatment
planning. Recently, deep learning has greatly improved the state-of-the-art
method in automatic segmentation. However, limitations in data availability in
the medical field can cause large variance and consequent overfitting to
medical image segmentation networks. In this study, we propose methods to
reduce the model variance of a rectal cancer segmentation network by adding a
rectum segmentation task and performing data augmentation; the geometric
correlation between the rectum and rectal cancer motivated the former approach.
Moreover, we propose a method to perform a bias-variance analysis within an
arbitrary region-of-interest (ROI) of a segmentation network, which we applied
to assess the efficacy of our approaches in reducing model variance. As a
result, adding a rectum segmentation task reduced the model variance of the
rectal cancer segmentation network within tumor regions by a factor of 0.90;
data augmentation further reduced the variance by a factor of 0.89. These
approaches also reduced the training duration by a factor of 0.96 and a further
factor of 0.78, respectively. Our approaches will improve the quality of rectal
cancer staging by increasing the accuracy of its automatic demarcation and by
providing rectum boundary information since rectal cancer staging requires the
demarcation of both rectum and rectal cancer. Besides such clinical benefits,
our method also enables segmentation networks to be assessed with bias-variance
analysis within an arbitrary ROI, such as a cancerous region.Comment: published at IEEE ACCES
Optimizing and Visualizing Deep Learning for Benign/Malignant Classification in Breast Tumors
Breast cancer has the highest incidence and second highest mortality rate for
women in the US. Our study aims to utilize deep learning for benign/malignant
classification of mammogram tumors using a subset of cases from the Digital
Database of Screening Mammography (DDSM). Though it was a small dataset from
the view of Deep Learning (about 1000 patients), we show that currently state
of the art architectures of deep learning can find a robust signal, even when
trained from scratch. Using convolutional neural networks (CNNs), we are able
to achieve an accuracy of 85% and an ROC AUC of 0.91, while leading
hand-crafted feature based methods are only able to achieve an accuracy of 71%.
We investigate an amalgamation of architectures to show that our best result is
reached with an ensemble of the lightweight GoogLe Nets tasked with
interpreting both the coronal caudal view and the mediolateral oblique view,
simply averaging the probability scores of both views to make the final
prediction. In addition, we have created a novel method to visualize what
features the neural network detects for the benign/malignant classification,
and have correlated those features with well known radiological features, such
as spiculation. Our algorithm significantly improves existing classification
methods for mammography lesions and identifies features that correlate with
established clinical markers
A Deep Journey into Super-resolution: A survey
Deep convolutional networks based super-resolution is a fast-growing field
with numerous practical applications. In this exposition, we extensively
compare 30+ state-of-the-art super-resolution Convolutional Neural Networks
(CNNs) over three classical and three recently introduced challenging datasets
to benchmark single image super-resolution. We introduce a taxonomy for
deep-learning based super-resolution networks that groups existing methods into
nine categories including linear, residual, multi-branch, recursive,
progressive, attention-based and adversarial designs. We also provide
comparisons between the models in terms of network complexity, memory
footprint, model input and output, learning details, the type of network losses
and important architectural differences (e.g., depth, skip-connections,
filters). The extensive evaluation performed, shows the consistent and rapid
growth in the accuracy in the past few years along with a corresponding boost
in model complexity and the availability of large-scale datasets. It is also
observed that the pioneering methods identified as the benchmark have been
significantly outperformed by the current contenders. Despite the progress in
recent years, we identify several shortcomings of existing techniques and
provide future research directions towards the solution of these open problems.Comment: Accepted in ACM Computing Survey
CRDN: Cascaded Residual Dense Networks for Dynamic MR Imaging with Edge-enhanced Loss Constraint
Dynamic magnetic resonance (MR) imaging has generated great research
interest, as it can provide both spatial and temporal information for clinical
diagnosis. However, slow imaging speed or long scanning time is still one of
the challenges for dynamic MR imaging. Most existing methods reconstruct
Dynamic MR images from incomplete k-space data under the guidance of compressed
sensing (CS) or low rank theory, which suffer from long iterative
reconstruction time. Recently, deep learning has shown great potential in
accelerating dynamic MR. Our previous work proposed a dynamic MR imaging method
with both k-space and spatial prior knowledge integrated via multi-supervised
network training. Nevertheless, there was still a certain degree of smooth in
the reconstructed images at high acceleration factors. In this work, we propose
cascaded residual dense networks for dynamic MR imaging with edge-enhance loss
constraint, dubbed as CRDN. Specifically, the cascaded residual dense networks
fully exploit the hierarchical features from all the convolutional layers with
both local and global feature fusion. We further utilize the total variation
(TV) loss function, which has the edge enhancement properties, for training the
networks
Deep convolutional networks for pancreas segmentation in CT imaging
Automatic organ segmentation is an important prerequisite for many
computer-aided diagnosis systems. The high anatomical variability of organs in
the abdomen, such as the pancreas, prevents many segmentation methods from
achieving high accuracies when compared to other segmentation of organs like
the liver, heart or kidneys. Recently, the availability of large annotated
training sets and the accessibility of affordable parallel computing resources
via GPUs have made it feasible for "deep learning" methods such as
convolutional networks (ConvNets) to succeed in image classification tasks.
These methods have the advantage that used classification features are trained
directly from the imaging data. We present a fully-automated bottom-up method
for pancreas segmentation in computed tomography (CT) images of the abdomen.
The method is based on hierarchical coarse-to-fine classification of local
image regions (superpixels). Superpixels are extracted from the abdominal
region using Simple Linear Iterative Clustering (SLIC). An initial probability
response map is generated, using patch-level confidences and a two-level
cascade of random forest classifiers, from which superpixel regions with
probabilities larger 0.5 are retained. These retained superpixels serve as a
highly sensitive initial input of the pancreas and its surroundings to a
ConvNet that samples a bounding box around each superpixel at different scales
(and random non-rigid deformations at training time) in order to assign a more
distinct probability of each superpixel region being pancreas or not. We
evaluate our method on CT images of 82 patients (60 for training, 2 for
validation, and 20 for testing). Using ConvNets we achieve average Dice scores
of 68%+-10% (range, 43-80%) in testing. This shows promise for accurate
pancreas segmentation, using a deep learning approach and compares favorably to
state-of-the-art methods.Comment: SPIE Medical Imaging conference, Orlando, FL, USA: SPIE Proceedings |
Volume 9413 | Classificatio
Baseline CNN structure analysis for facial expression recognition
We present a baseline convolutional neural network (CNN) structure and image
preprocessing methodology to improve facial expression recognition algorithm
using CNN. To analyze the most efficient network structure, we investigated
four network structures that are known to show good performance in facial
expression recognition. Moreover, we also investigated the effect of input
image preprocessing methods. Five types of data input (raw, histogram
equalization, isotropic smoothing, diffusion-based normalization, difference of
Gaussian) were tested, and the accuracy was compared. We trained 20 different
CNN models (4 networks x 5 data input types) and verified the performance of
each network with test images from five different databases. The experiment
result showed that a three-layer structure consisting of a simple convolutional
and a max pooling layer with histogram equalization image input was the most
efficient. We describe the detailed training procedure and analyze the result
of the test accuracy based on considerable observation.Comment: 6 pages, RO-MAN2016 Conferenc
Fast and Efficient Zero-Learning Image Fusion
We propose a real-time image fusion method using pre-trained neural networks.
Our method generates a single image containing features from multiple sources.
We first decompose images into a base layer representing large scale intensity
variations, and a detail layer containing small scale changes. We use visual
saliency to fuse the base layers, and deep feature maps extracted from a
pre-trained neural network to fuse the detail layers. We conduct ablation
studies to analyze our method's parameters such as decomposition filters,
weight construction methods, and network depth and architecture. Then, we
validate its effectiveness and speed on thermal, medical, and multi-focus
fusion. We also apply it to multiple image inputs such as multi-exposure
sequences. The experimental results demonstrate that our technique achieves
state-of-the-art performance in visual quality, objective assessment, and
runtime efficiency.Comment: 13 pages, 10 figure
Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction
Following the success of deep learning in a wide range of applications,
neural network-based machine learning techniques have received interest as a
means of accelerating magnetic resonance imaging (MRI). A number of ideas
inspired by deep learning techniques from computer vision and image processing
have been successfully applied to non-linear image reconstruction in the spirit
of compressed sensing for both low dose computed tomography and accelerated
MRI. The additional integration of multi-coil information to recover missing
k-space lines in the MRI reconstruction process, is still studied less
frequently, even though it is the de-facto standard for currently used
accelerated MR acquisitions. This manuscript provides an overview of the recent
machine learning approaches that have been proposed specifically for improving
parallel imaging. A general background introduction to parallel MRI is given
that is structured around the classical view of image space and k-space based
methods. Both linear and non-linear methods are covered, followed by a
discussion of recent efforts to further improve parallel imaging using machine
learning, and specifically using artificial neural networks. Image-domain based
techniques that introduce improved regularizers are covered as well as k-space
based methods, where the focus is on better interpolation strategies using
neural networks. Issues and open problems are discussed as well as recent
efforts for producing open datasets and benchmarks for the community.Comment: 14 pages, 7 figure
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
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