163 research outputs found
A Literature Review of the "Burning Money" Behavior of Internet Products
This article mainly studies the current status of the operation of the “burning money model” of Internet products, expounds and analyzes the disadvantages of this operating model, studies the practical effects brought by the “burning money model”, and explores the development direction of future Internet products. Domestic scholars have done a lot of research on the development of Internet products in the future and have achieved certain results. This article aims to discuss the development model of China’s Internet products, summarize the current “burning money” behavior in the operation process of Internet products, and combine the development situation of China’s Internet in the new era to make suggestions for the future development of Internet products
Knowledge Base Aware Semantic Communication in Vehicular Networks
Semantic communication (SemCom) has recently been considered a promising
solution for the inevitable crisis of scarce communication resources. This
trend stimulates us to explore the potential of applying SemCom to vehicular
networks, which normally consume a tremendous amount of resources to achieve
stringent requirements on high reliability and low latency. Unfortunately, the
unique background knowledge matching mechanism in SemCom makes it challenging
to realize efficient vehicle-to-vehicle service provisioning for multiple users
at the same time. To this end, this paper identifies and jointly addresses two
fundamental problems of knowledge base construction (KBC) and vehicle service
pairing (VSP) inherently existing in SemCom-enabled vehicular networks.
Concretely, we first derive the knowledge matching based queuing latency
specific for semantic data packets, and then formulate a latency-minimization
problem subject to several KBC and VSP related reliability constraints.
Afterward, a SemCom-empowered Service Supplying Solution (S) is
proposed along with the theoretical analysis of its optimality guarantee.
Simulation results demonstrate the superiority of S in terms of
average queuing latency, semantic data packet throughput, and user knowledge
preference satisfaction compared with two different benchmarks.Comment: This paper has been accepted for publication by 2023 IEEE
International Conference on Communications (ICC 2023). Copyright may be
transferred without notice, after which this version may no longer be
accessible. arXiv admin note: substantial text overlap with arXiv:2302.1199
Liver Tumor Screening and Diagnosis in CT with Pixel-Lesion-Patient Network
Liver tumor segmentation and classification are important tasks in computer
aided diagnosis. We aim to address three problems: liver tumor screening and
preliminary diagnosis in non-contrast computed tomography (CT), and
differential diagnosis in dynamic contrast-enhanced CT. A novel framework named
Pixel-Lesion-pAtient Network (PLAN) is proposed. It uses a mask transformer to
jointly segment and classify each lesion with improved anchor queries and a
foreground-enhanced sampling loss. It also has an image-wise classifier to
effectively aggregate global information and predict patient-level diagnosis. A
large-scale multi-phase dataset is collected containing 939 tumor patients and
810 normal subjects. 4010 tumor instances of eight types are extensively
annotated. On the non-contrast tumor screening task, PLAN achieves 95% and 96%
in patient-level sensitivity and specificity. On contrast-enhanced CT, our
lesion-level detection precision, recall, and classification accuracy are 92%,
89%, and 86%, outperforming widely used CNN and transformers for lesion
segmentation. We also conduct a reader study on a holdout set of 250 cases.
PLAN is on par with a senior human radiologist, showing the clinical
significance of our results.Comment: MICCAI 2023, code:
https://github.com/alibaba-damo-academy/pixel-lesion-patient-networ
Cluster-Induced Mask Transformers for Effective Opportunistic Gastric Cancer Screening on Non-contrast CT Scans
Gastric cancer is the third leading cause of cancer-related mortality
worldwide, but no guideline-recommended screening test exists. Existing methods
can be invasive, expensive, and lack sensitivity to identify early-stage
gastric cancer. In this study, we explore the feasibility of using a deep
learning approach on non-contrast CT scans for gastric cancer detection. We
propose a novel cluster-induced Mask Transformer that jointly segments the
tumor and classifies abnormality in a multi-task manner. Our model incorporates
learnable clusters that encode the texture and shape prototypes of gastric
cancer, utilizing self- and cross-attention to interact with convolutional
features. In our experiments, the proposed method achieves a sensitivity of
85.0% and specificity of 92.6% for detecting gastric tumors on a hold-out test
set consisting of 100 patients with cancer and 148 normal. In comparison, two
radiologists have an average sensitivity of 73.5% and specificity of 84.3%. We
also obtain a specificity of 97.7% on an external test set with 903 normal
cases. Our approach performs comparably to established state-of-the-art gastric
cancer screening tools like blood testing and endoscopy, while also being more
sensitive in detecting early-stage cancer. This demonstrates the potential of
our approach as a novel, non-invasive, low-cost, and accurate method for
opportunistic gastric cancer screening.Comment: MICCAI 202
Improved Prognostic Prediction of Pancreatic Cancer Using Multi-Phase CT by Integrating Neural Distance and Texture-Aware Transformer
Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer in which
the tumor-vascular involvement greatly affects the resectability and, thus,
overall survival of patients. However, current prognostic prediction methods
fail to explicitly and accurately investigate relationships between the tumor
and nearby important vessels. This paper proposes a novel learnable neural
distance that describes the precise relationship between the tumor and vessels
in CT images of different patients, adopting it as a major feature for
prognosis prediction. Besides, different from existing models that used CNNs or
LSTMs to exploit tumor enhancement patterns on dynamic contrast-enhanced CT
imaging, we improved the extraction of dynamic tumor-related texture features
in multi-phase contrast-enhanced CT by fusing local and global features using
CNN and transformer modules, further enhancing the features extracted across
multi-phase CT images. We extensively evaluated and compared the proposed
method with existing methods in the multi-center (n=4) dataset with 1,070
patients with PDAC, and statistical analysis confirmed its clinical
effectiveness in the external test set consisting of three centers. The
developed risk marker was the strongest predictor of overall survival among
preoperative factors and it has the potential to be combined with established
clinical factors to select patients at higher risk who might benefit from
neoadjuvant therapy.Comment: MICCAI 202
CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans
Human readers or radiologists routinely perform full-body multi-organ
multi-disease detection and diagnosis in clinical practice, while most medical
AI systems are built to focus on single organs with a narrow list of a few
diseases. This might severely limit AI's clinical adoption. A certain number of
AI models need to be assembled non-trivially to match the diagnostic process of
a human reading a CT scan. In this paper, we construct a Unified Tumor
Transformer (CancerUniT) model to jointly detect tumor existence & location and
diagnose tumor characteristics for eight major cancers in CT scans. CancerUniT
is a query-based Mask Transformer model with the output of multi-tumor
prediction. We decouple the object queries into organ queries, tumor detection
queries and tumor diagnosis queries, and further establish hierarchical
relationships among the three groups. This clinically-inspired architecture
effectively assists inter- and intra-organ representation learning of tumors
and facilitates the resolution of these complex, anatomically related
multi-organ cancer image reading tasks. CancerUniT is trained end-to-end using
a curated large-scale CT images of 10,042 patients including eight major types
of cancers and occurring non-cancer tumors (all are pathology-confirmed with 3D
tumor masks annotated by radiologists). On the test set of 631 patients,
CancerUniT has demonstrated strong performance under a set of clinically
relevant evaluation metrics, substantially outperforming both multi-disease
methods and an assembly of eight single-organ expert models in tumor detection,
segmentation, and diagnosis. This moves one step closer towards a universal
high performance cancer screening tool.Comment: ICCV 2023 Camera Ready Versio
Neutrino Physics with JUNO
The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multi-purposeunderground liquid scintillator detector, was proposed with the determinationof the neutrino mass hierarchy as a primary physics goal. It is also capable ofobserving neutrinos from terrestrial and extra-terrestrial sources, includingsupernova burst neutrinos, diffuse supernova neutrino background, geoneutrinos,atmospheric neutrinos, solar neutrinos, as well as exotic searches such asnucleon decays, dark matter, sterile neutrinos, etc. We present the physicsmotivations and the anticipated performance of the JUNO detector for variousproposed measurements. By detecting reactor antineutrinos from two power plantsat 53-km distance, JUNO will determine the neutrino mass hierarchy at a 3-4sigma significance with six years of running. The measurement of antineutrinospectrum will also lead to the precise determination of three out of the sixoscillation parameters to an accuracy of better than 1\%. Neutrino burst from atypical core-collapse supernova at 10 kpc would lead to ~5000inverse-beta-decay events and ~2000 all-flavor neutrino-proton elasticscattering events in JUNO. Detection of DSNB would provide valuable informationon the cosmic star-formation rate and the average core-collapsed neutrinoenergy spectrum. Geo-neutrinos can be detected in JUNO with a rate of ~400events per year, significantly improving the statistics of existing geoneutrinosamples. The JUNO detector is sensitive to several exotic searches, e.g. protondecay via the decay channel. The JUNO detector will providea unique facility to address many outstanding crucial questions in particle andastrophysics. It holds the great potential for further advancing our quest tounderstanding the fundamental properties of neutrinos, one of the buildingblocks of our Universe
Potential of Core-Collapse Supernova Neutrino Detection at JUNO
JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve
Detection of the Diffuse Supernova Neutrino Background with JUNO
As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO
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