142 research outputs found
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
U-Net and its variants for medical image segmentation: theory and applications
U-net is an image segmentation technique developed primarily for medical
image analysis that can precisely segment images using a scarce amount of
training data. These traits provide U-net with a very high utility within the
medical imaging community and have resulted in extensive adoption of U-net as
the primary tool for segmentation tasks in medical imaging. The success of
U-net is evident in its widespread use in all major image modalities from CT
scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a
segmentation tool, there have been instances of the use of U-net in other
applications. As the potential of U-net is still increasing, in this review we
look at the various developments that have been made in the U-net architecture
and provide observations on recent trends. We examine the various innovations
that have been made in deep learning and discuss how these tools facilitate
U-net. Furthermore, we look at image modalities and application areas where
U-net has been applied.Comment: 42 pages, in IEEE Acces
An automatic COVID-19 CT segmentation network using spatial and channel attention mechanism
The coronavirus disease (COVID-19) pandemic has led to a devastating effect
on the global public health. Computed Tomography (CT) is an effective tool in
the screening of COVID-19. It is of great importance to rapidly and accurately
segment COVID-19 from CT to help diagnostic and patient monitoring. In this
paper, we propose a U-Net based segmentation network using attention mechanism.
As not all the features extracted from the encoders are useful for
segmentation, we propose to incorporate an attention mechanism including a
spatial and a channel attention, to a U-Net architecture to re-weight the
feature representation spatially and channel-wise to capture rich contextual
relationships for better feature representation. In addition, the focal tversky
loss is introduced to deal with small lesion segmentation. The experiment
results, evaluated on a COVID-19 CT segmentation dataset where 473 CT slices
are available, demonstrate the proposed method can achieve an accurate and
rapid segmentation on COVID-19 segmentation. The method takes only 0.29 second
to segment a single CT slice. The obtained Dice Score, Sensitivity and
Specificity are 83.1%, 86.7% and 99.3%, respectively.Comment: 14 pages, 6 figure
U-net and its variants for medical image segmentation: A review of theory and applications
U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to Xrays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net’s potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net
Two Independent Teachers are Better Role Model
Recent deep learning models have attracted substantial attention in infant
brain analysis. These models have performed state-of-the-art performance, such
as semi-supervised techniques (e.g., Temporal Ensembling, mean teacher).
However, these models depend on an encoder-decoder structure with stacked local
operators to gather long-range information, and the local operators limit the
efficiency and effectiveness. Besides, the data contain different tissue
properties () such as and . One major limitation of these models
is that they use both data as inputs to the segment process, i.e., the models
are trained on the dataset once, and it requires much computational and memory
requirements during inference. In this work, we address the above limitations
by designing a new deep-learning model, called 3D-DenseUNet, which works as
adaptable global aggregation blocks in down-sampling to solve the issue of
spatial information loss. The self-attention module connects the down-sampling
blocks to up-sampling blocks, and integrates the feature maps in three
dimensions of spatial and channel, effectively improving the representation
potential and discriminating ability of the model. Additionally, we propose a
new method called Two Independent Teachers (), that summarizes the model
weights instead of label predictions. Each teacher model is trained on
different types of brain data, and , respectively. Then, a fuse model
is added to improve test accuracy and enable training with fewer parameters and
labels compared to the Temporal Ensembling method without modifying the network
architecture. Empirical results demonstrate the effectiveness of the proposed
method.Comment: This manuscript contains 14 pages, 7 figures. We have submitted the
manuscript to Journal of IEEE Transactions on Medical Imaging (TMI) in June
202
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