714 research outputs found
Improving Autonomous Vehicle Mapping and Navigation in Work Zones Using Crowdsourcing Vehicle Trajectories
Prevalent solutions for Connected and Autonomous vehicle (CAV) mapping
include high definition map (HD map) or real-time Simultaneous Localization and
Mapping (SLAM). Both methods only rely on vehicle itself (onboard sensors or
embedded maps) and can not adapt well to temporarily changed drivable areas
such as work zones. Navigating CAVs in such areas heavily relies on how the
vehicle defines drivable areas based on perception information. Difficulties in
improving perception accuracy and ensuring the correct interpretation of
perception results are challenging to the vehicle in these situations. This
paper presents a prototype that introduces crowdsourcing trajectories
information into the mapping process to enhance CAV's understanding on the
drivable area and traffic rules. A Gaussian Mixture Model (GMM) is applied to
construct the temporarily changed drivable area and occupancy grid map (OGM)
based on crowdsourcing trajectories. The proposed method is compared with SLAM
without any human driving information. Our method has adapted well with the
downstream path planning and vehicle control module, and the CAV did not
violate driving rule, which a pure SLAM method did not achieve.Comment: Presented at TRBAM. Journal version in progres
SSHNN: Semi-Supervised Hybrid NAS Network for Echocardiographic Image Segmentation
Accurate medical image segmentation especially for echocardiographic images
with unmissable noise requires elaborate network design. Compared with manual
design, Neural Architecture Search (NAS) realizes better segmentation results
due to larger search space and automatic optimization, but most of the existing
methods are weak in layer-wise feature aggregation and adopt a ``strong
encoder, weak decoder" structure, insufficient to handle global relationships
and local details. To resolve these issues, we propose a novel semi-supervised
hybrid NAS network for accurate medical image segmentation termed SSHNN. In
SSHNN, we creatively use convolution operation in layer-wise feature fusion
instead of normalized scalars to avoid losing details, making NAS a stronger
encoder. Moreover, Transformers are introduced for the compensation of global
context and U-shaped decoder is designed to efficiently connect global context
with local features. Specifically, we implement a semi-supervised algorithm
Mean-Teacher to overcome the limited volume problem of labeled medical image
dataset. Extensive experiments on CAMUS echocardiography dataset demonstrate
that SSHNN outperforms state-of-the-art approaches and realizes accurate
segmentation. Code will be made publicly available.Comment: Submitted to ICASSP202
Global-Regularized Neighborhood Regression for Efficient Zero-Shot Texture Anomaly Detection
Texture surface anomaly detection finds widespread applications in industrial
settings. However, existing methods often necessitate gathering numerous
samples for model training. Moreover, they predominantly operate within a
close-set detection framework, limiting their ability to identify anomalies
beyond the training dataset. To tackle these challenges, this paper introduces
a novel zero-shot texture anomaly detection method named Global-Regularized
Neighborhood Regression (GRNR). Unlike conventional approaches, GRNR can detect
anomalies on arbitrary textured surfaces without any training data or cost.
Drawing from human visual cognition, GRNR derives two intrinsic prior supports
directly from the test texture image: local neighborhood priors characterized
by coherent similarities and global normality priors featuring typical normal
patterns. The fundamental principle of GRNR involves utilizing the two
extracted intrinsic support priors for self-reconstructive regression of the
query sample. This process employs the transformation facilitated by local
neighbor support while being regularized by global normality support, aiming to
not only achieve visually consistent reconstruction results but also preserve
normality properties. We validate the effectiveness of GRNR across various
industrial scenarios using eight benchmark datasets, demonstrating its superior
detection performance without the need for training data. Remarkably, our
method is applicable for open-set texture defect detection and can even surpass
existing vanilla approaches that require extensive training.Comment: SUBMISSION TO IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS:
SYSTEM
Generative Quanta Color Imaging
The astonishing development of single-photon cameras has created an
unprecedented opportunity for scientific and industrial imaging. However, the
high data throughput generated by these 1-bit sensors creates a significant
bottleneck for low-power applications. In this paper, we explore the
possibility of generating a color image from a single binary frame of a
single-photon camera. We evidently find this problem being particularly
difficult to standard colorization approaches due to the substantial degree of
exposure variation. The core innovation of our paper is an exposure synthesis
model framed under a neural ordinary differential equation (Neural ODE) that
allows us to generate a continuum of exposures from a single observation. This
innovation ensures consistent exposure in binary images that colorizers take
on, resulting in notably enhanced colorization. We demonstrate applications of
the method in single-image and burst colorization and show superior generative
performance over baselines. Project website can be found at
https://vishal-s-p.github.io/projects/2023/generative_quanta_color.html.Comment: Accepted at IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), 202
Observation of spin-tensor induced topological phase transitions of triply degenerate points with a trapped ion
Triply degenerate points (TDPs), which correspond to new types of topological
semimetals, can support novel quasiparticles possessing effective integer spins
while preserving Fermi statistics. Here by mapping the momentum space to the
parameter space of a three-level system in a trapped ion, we experimentally
explore the transitions between different types of TDPs driven by
spin-tensor--momentum couplings. We observe the phase transitions between TDPs
with different topological charges by measuring the Berry flux on a loop
surrounding the gap-closing lines, and the jump of the Berry flux gives the
jump of the topological charge (up to a factor) across the transitions.
For the Berry flux measurement, we employ a new method by examining the
geometric rotations of both spin vectors and tensors, which lead to a
generalized solid angle equal to the Berry flux. The controllability of
multi-level ion offers a versatile platform to study high-spin physics and our
work paves the way to explore novel topological phenomena therein.Comment: 9 pages, 10 figure
Generation and applications of synthetic computed tomography images for neurosurgical planning
OBJECTIVE: CT and MRI are synergistic in the information provided for neurosurgical planning. While obtaining both types of images lends unique data from each, doing so adds to cost and exposes patients to additional ionizing radiation after MRI has been performed. Cross-modal synthesis of high-resolution CT images from MRI sequences offers an appealing solution. The authors therefore sought to develop a deep learning conditional generative adversarial network (cGAN) which performs this synthesis. METHODS: Preoperative paired CT and contrast-enhanced MR images were collected for patients with meningioma, pituitary tumor, vestibular schwannoma, and cerebrovascular disease. CT and MR images were denoised, field corrected, and coregistered. MR images were fed to a cGAN that exported a "synthetic" CT scan. The accuracy of synthetic CT images was assessed objectively using the quantitative similarity metrics as well as by clinical features such as sella and internal auditory canal (IAC) dimensions and mastoid/clinoid/sphenoid aeration. RESULTS: A total of 92,981 paired CT/MR images obtained in 80 patients were used for training/testing, and 10,068 paired images from 10 patients were used for external validation. Synthetic CT images reconstructed the bony skull base and convexity with relatively high accuracy. Measurements of the sella and IAC showed a median relative error between synthetic CT scans and ground truth images of 6%, with greater variability in IAC reconstruction compared with the sella. Aerations in the mastoid, clinoid, and sphenoid regions were generally captured, although there was heterogeneity in finer air cell septations. Performance varied based on pathology studied, with the highest limitation observed in evaluating meningiomas with intratumoral calcifications or calvarial invasion. CONCLUSIONS: The generation of high-resolution CT scans from MR images through cGAN offers promise for a wide range of applications in cranial and spinal neurosurgery, especially as an adjunct for preoperative evaluation. Optimizing cGAN performance on specific anatomical regions may increase its clinical viability.</p
Clinical Efficacy of Temozolomide and Its Predictors in Aggressive Pituitary Tumors and Pituitary Carcinomas: A Systematic Review and Meta-Analysis
Background: A growing number of evidences suggest that TMZ applications can generate impressive benefits for APT and PC patients. However, the definite role of TMZ for individuals remains unclarified due to the variation between studies. And the predictive factors to alter its efficacy remain debatable.Objective: To evaluate the long-term effectiveness and safety profile of TMZ in the treatment of pituitary malignancies, and delineate the predictors during its clinical employment.Results: A literature retrieval was conducted from online databases for studies published up to December 31, 2020. Twenty one studies involving 429 patients were identified. TMZ exhibited 41% radiological overall response rate (rORR). The biochemical response rate was determinate in 53% of the functioning subset. Two-year and 4-year survival rate were 79 and 61%, respectively. TMZ prolonged the median PFS and OS as 20.18 and 40.24 months. TMZ-related adverse events occurred in 19% of patients. Regarding predictors of TMZ response, rORR was dramatically improved in patients with low/intermediate MGMT expression than those with high-MGMT (>50%) (p < 0.001). The benefit of TMZ varied according to functioning subtype of patients, with greater antitumor activities in functioning subgroups and fewer activities in non-functioning sets (p < 0.001). Notably, the concomitant therapy of radiotherapy and TMZ significantly increased the rORR (p = 0.007).Conclusion: TMZ elicits clinical benefits with moderate adverse events in APT and PC patients. MGMT expression and clinical subtype of secreting function might be vital predictors of TMZ efficacy. In the future, the combination of radiotherapy with TMZ may further improve the clinical outcomes than TMZ monotherapy
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