17,249 research outputs found
Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks
Magnetic resonance image (MRI) in high spatial resolution provides detailed
anatomical information and is often necessary for accurate quantitative
analysis. However, high spatial resolution typically comes at the expense of
longer scan time, less spatial coverage, and lower signal to noise ratio (SNR).
Single Image Super-Resolution (SISR), a technique aimed to restore
high-resolution (HR) details from one single low-resolution (LR) input image,
has been improved dramatically by recent breakthroughs in deep learning. In
this paper, we introduce a new neural network architecture, 3D Densely
Connected Super-Resolution Networks (DCSRN) to restore HR features of
structural brain MR images. Through experiments on a dataset with 1,113
subjects, we demonstrate that our network outperforms bicubic interpolation as
well as other deep learning methods in restoring 4x resolution-reduced images.Comment: Accepted by ISBI'1
High frame-rate cardiac ultrasound imaging with deep learning
Cardiac ultrasound imaging requires a high frame rate in order to capture
rapid motion. This can be achieved by multi-line acquisition (MLA), where
several narrow-focused received lines are obtained from each wide-focused
transmitted line. This shortens the acquisition time at the expense of
introducing block artifacts. In this paper, we propose a data-driven
learning-based approach to improve the MLA image quality. We train an
end-to-end convolutional neural network on pairs of real ultrasound cardiac
data, acquired through MLA and the corresponding single-line acquisition (SLA).
The network achieves a significant improvement in image quality for both
and line MLA resulting in a decorrelation measure similar to that of SLA
while having the frame rate of MLA.Comment: To appear in the Proceedings of MICCAI, 201
The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
Training of neural networks for automated diagnosis of pigmented skin lesions
is hampered by the small size and lack of diversity of available datasets of
dermatoscopic images. We tackle this problem by releasing the HAM10000 ("Human
Against Machine with 10000 training images") dataset. We collected
dermatoscopic images from different populations acquired and stored by
different modalities. Given this diversity we had to apply different
acquisition and cleaning methods and developed semi-automatic workflows
utilizing specifically trained neural networks. The final dataset consists of
10015 dermatoscopic images which are released as a training set for academic
machine learning purposes and are publicly available through the ISIC archive.
This benchmark dataset can be used for machine learning and for comparisons
with human experts. Cases include a representative collection of all important
diagnostic categories in the realm of pigmented lesions. More than 50% of
lesions have been confirmed by pathology, while the ground truth for the rest
of the cases was either follow-up, expert consensus, or confirmation by in-vivo
confocal microscopy
Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology
Stain variation is a phenomenon observed when distinct pathology laboratories
stain tissue slides that exhibit similar but not identical color appearance.
Due to this color shift between laboratories, convolutional neural networks
(CNNs) trained with images from one lab often underperform on unseen images
from the other lab. Several techniques have been proposed to reduce the
generalization error, mainly grouped into two categories: stain color
augmentation and stain color normalization. The former simulates a wide variety
of realistic stain variations during training, producing stain-invariant CNNs.
The latter aims to match training and test color distributions in order to
reduce stain variation. For the first time, we compared some of these
techniques and quantified their effect on CNN classification performance using
a heterogeneous dataset of hematoxylin and eosin histopathology images from 4
organs and 9 pathology laboratories. Additionally, we propose a novel
unsupervised method to perform stain color normalization using a neural
network. Based on our experimental results, we provide practical guidelines on
how to use stain color augmentation and stain color normalization in future
computational pathology applications.Comment: Accepted in the Medical Image Analysis journa
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