1,855 research outputs found
Software Input Pattern and Test Coverage using Computational Linguistics on Structured Data
This disclosure describes computational linguistics techniques for software input patterns and test coverage. Structured input data which can have arbitrary and evolving schema, obtained from production software and from testbeds, are tokenized using tree traversal to generate vocabulary, unigram statistics, and bags of words (BoW). BoWs are subjected to statistical analysis to programmatically and intelligently discover software usage patterns in production, to identify test coverage, and to flag gaps in testing
Hypergraph Neural Networks
In this paper, we present a hypergraph neural networks (HGNN) framework for
data representation learning, which can encode high-order data correlation in a
hypergraph structure. Confronting the challenges of learning representation for
complex data in real practice, we propose to incorporate such data structure in
a hypergraph, which is more flexible on data modeling, especially when dealing
with complex data. In this method, a hyperedge convolution operation is
designed to handle the data correlation during representation learning. In this
way, traditional hypergraph learning procedure can be conducted using hyperedge
convolution operations efficiently. HGNN is able to learn the hidden layer
representation considering the high-order data structure, which is a general
framework considering the complex data correlations. We have conducted
experiments on citation network classification and visual object recognition
tasks and compared HGNN with graph convolutional networks and other traditional
methods. Experimental results demonstrate that the proposed HGNN method
outperforms recent state-of-the-art methods. We can also reveal from the
results that the proposed HGNN is superior when dealing with multi-modal data
compared with existing methods.Comment: Accepted in AAAI'201
Multi-Energy Blended CBCT Spectral Imaging Using a Spectral Modulator with Flying Focal Spot (SMFFS)
Cone-beam CT (CBCT) spectral imaging has great potential in medical and
industrial applications, but it is very challenging as scatter and spectral
effects are seriously twisted. In this work, we present the first attempt to
develop a stationary spectral modulator with flying focal spot (SMFFS)
technology as a promising, low-cost approach to accurately solving the X-ray
scattering problem and physically enabling spectral imaging in a unified
framework, and with no significant misalignment in data sampling of spectral
projections. Based on an in-depth analysis of optimal energy separation from
different combinations of modulator materials and thicknesses, we present a
practical design of a mixed two-dimensional spectral modulator that can
generate multi-energy blended CBCT spectral projections. To deal with the
twisted scatter-spectral challenge, we propose a novel scatter-decoupled
material decomposition (SDMD) method by taking advantage of a scatter
similarity in SMFFS. A Monte Carlo simulation is conducted to validate the
strong similarity of X-ray scatter distributions across the flying focal spot
positions. Both numerical simulations using a clinical abdominal CT dataset,
and physics experiments on a tabletop CBCT system using a GAMMEX multi-energy
CT phantom, are carried out to demonstrate the feasibility of our proposed SDMD
method for CBCT spectral imaging with SMFFS. In the physics experiments, the
mean relative errors in selected ROI for virtual monochromatic image (VMI) are
0.9\% for SMFFS, and 5.3\% and 16.9\% for 80/120 kV dual-energy cone-beam scan
with and without scatter correction, respectively. Our preliminary results show
that SMFFS can effectively improve the quantitative imaging performance of
CBCT.Comment: 10 pages, 13 figure
DeSAM: Decoupling Segment Anything Model for Generalizable Medical Image Segmentation
Deep learning based automatic medical image segmentation models often suffer
from domain shift, where the models trained on a source domain do not
generalize well to other unseen domains. As a vision foundation model with
powerful generalization capabilities, Segment Anything Model (SAM) shows
potential for improving the cross-domain robustness of medical image
segmentation. However, SAM and its fine-tuned models performed significantly
worse in fully automatic mode compared to when given manual prompts. Upon
further investigation, we discovered that the degradation in performance was
related to the coupling effect of poor prompts and mask segmentation. In fully
automatic mode, the presence of inevitable poor prompts (such as points outside
the mask or boxes significantly larger than the mask) can significantly mislead
mask generation. To address the coupling effect, we propose the decoupling SAM
(DeSAM). DeSAM modifies SAM's mask decoder to decouple mask generation and
prompt embeddings while leveraging pre-trained weights. We conducted
experiments on publicly available prostate cross-site datasets. The results
show that DeSAM improves dice score by an average of 8.96% (from 70.06% to
79.02%) compared to previous state-of-the-art domain generalization method.
Moreover, DeSAM can be trained on personal devices with entry-level GPU since
our approach does not rely on tuning the heavyweight image encoder. The code is
publicly available at https://github.com/yifangao112/DeSAM.Comment: 12 pages. The code is available at
https://github.com/yifangao112/DeSA
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