434 research outputs found
Predicting Alzheimer's Disease by Hierarchical Graph Convolution from Positron Emission Tomography Imaging
Imaging-based early diagnosis of Alzheimer Disease (AD) has become an
effective approach, especially by using nuclear medicine imaging techniques
such as Positron Emission Topography (PET). In various literature it has been
found that PET images can be better modeled as signals (e.g. uptake of
florbetapir) defined on a network (non-Euclidean) structure which is governed
by its underlying graph patterns of pathological progression and metabolic
connectivity. In order to effectively apply deep learning framework for PET
image analysis to overcome its limitation on Euclidean grid, we develop a
solution for 3D PET image representation and analysis under a generalized,
graph-based CNN architecture (PETNet), which analyzes PET signals defined on a
group-wise inferred graph structure. Computations in PETNet are defined in
non-Euclidean, graph (network) domain, as it performs feature extraction by
convolution operations on spectral-filtered signals on the graph and pooling
operations based on hierarchical graph clustering. Effectiveness of the PETNet
is evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset,
which shows improved performance over both deep learning and other machine
learning-based methods.Comment: Jiaming Guo, Wei Qiu and Xiang Li contribute equally to this wor
A Comprehensive Comparison of Projections in Omnidirectional Super-Resolution
Super-Resolution (SR) has gained increasing research attention over the past
few years. With the development of Deep Neural Networks (DNNs), many
super-resolution methods based on DNNs have been proposed. Although most of
these methods are aimed at ordinary frames, there are few works on
super-resolution of omnidirectional frames. In these works, omnidirectional
frames are projected from the 3D sphere to a 2D plane by Equi-Rectangular
Projection (ERP). Although ERP has been widely used for projection, it has
severe projection distortion near poles. Current DNN-based SR methods use 2D
convolution modules, which is more suitable for the regular grid. In this
paper, we find that different projection methods have great impact on the
performance of DNNs. To study this problem, a comprehensive comparison of
projections in omnidirectional super-resolution is conducted. We compare the SR
results of different projection methods. Experimental results show that
Equi-Angular cube map projection (EAC), which has minimal distortion, achieves
the best result in terms of WS-PSNR compared with other projections. Code and
data will be released.Comment: Accepted to ICASSP202
CABM: Content-Aware Bit Mapping for Single Image Super-Resolution Network with Large Input
With the development of high-definition display devices, the practical
scenario of Super-Resolution (SR) usually needs to super-resolve large input
like 2K to higher resolution (4K/8K). To reduce the computational and memory
cost, current methods first split the large input into local patches and then
merge the SR patches into the output. These methods adaptively allocate a
subnet for each patch. Quantization is a very important technique for network
acceleration and has been used to design the subnets. Current methods train an
MLP bit selector to determine the propoer bit for each layer. However, they
uniformly sample subnets for training, making simple subnets overfitted and
complicated subnets underfitted. Therefore, the trained bit selector fails to
determine the optimal bit. Apart from this, the introduced bit selector brings
additional cost to each layer of the SR network. In this paper, we propose a
novel method named Content-Aware Bit Mapping (CABM), which can remove the bit
selector without any performance loss. CABM also learns a bit selector for each
layer during training. After training, we analyze the relation between the edge
information of an input patch and the bit of each layer. We observe that the
edge information can be an effective metric for the selected bit. Therefore, we
design a strategy to build an Edge-to-Bit lookup table that maps the edge score
of a patch to the bit of each layer during inference. The bit configuration of
SR network can be determined by the lookup tables of all layers. Our strategy
can find better bit configuration, resulting in more efficient mixed precision
networks. We conduct detailed experiments to demonstrate the generalization
ability of our method. The code will be released.Comment: Accepted to CVPR202
Swin Transformer-Based CSI Feedback for Massive MIMO
For massive multiple-input multiple-output systems in the frequency division
duplex (FDD) mode, accurate downlink channel state information (CSI) is
required at the base station (BS). However, the increasing number of transmit
antennas aggravates the feedback overhead of CSI. Recently, deep learning (DL)
has shown considerable potential to reduce CSI feedback overhead. In this
paper, we propose a Swin Transformer-based autoencoder network called SwinCFNet
for the CSI feedback task. In particular, the proposed method can effectively
capture the long-range dependence information of CSI. Moreover, we explore the
impact of the number of Swin Transformer blocks and the dimension of feature
channels on the performance of SwinCFNet. Experimental results show that
SwinCFNet significantly outperforms other DL-based methods with comparable
model sizes, especially for the outdoor scenario
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