76 research outputs found
Tuning Pre-trained Model via Moment Probing
Recently, efficient fine-tuning of large-scale pre-trained models has
attracted increasing research interests, where linear probing (LP) as a
fundamental module is involved in exploiting the final representations for
task-dependent classification. However, most of the existing methods focus on
how to effectively introduce a few of learnable parameters, and little work
pays attention to the commonly used LP module. In this paper, we propose a
novel Moment Probing (MP) method to further explore the potential of LP.
Distinguished from LP which builds a linear classification head based on the
mean of final features (e.g., word tokens for ViT) or classification tokens,
our MP performs a linear classifier on feature distribution, which provides the
stronger representation ability by exploiting richer statistical information
inherent in features. Specifically, we represent feature distribution by its
characteristic function, which is efficiently approximated by using first- and
second-order moments of features. Furthermore, we propose a multi-head
convolutional cross-covariance (MHC) to compute second-order moments in an
efficient and effective manner. By considering that MP could affect feature
learning, we introduce a partially shared module to learn two recalibrating
parameters (PSRP) for backbones based on MP, namely MP. Extensive
experiments on ten benchmarks using various models show that our MP
significantly outperforms LP and is competitive with counterparts at less
training cost, while our MP achieves state-of-the-art performance.Comment: Accepted to ICCV 2023; Project Page:
https://github.com/mingzeG/Moment-Probin
Demand Response Method Considering Multiple Types of Flexible Loads in Industrial Parks
With the rapid development of the energy internet, the proportion of flexible
loads in smart grid is getting much higher than before. It is highly important
to model flexible loads based on demand response. Therefore, a new demand
response method considering multiple flexible loads is proposed in this paper
to character the integrated demand response (IDR) resources. Firstly, a
physical process analytical deduction (PPAD) model is proposed to improve the
classification of flexible loads in industrial parks. Scenario generation, data
point augmentation, and smooth curves under various operating conditions are
considered to enhance the applicability of the model. Secondly, in view of the
strong volatility and poor modeling effect of Wasserstein-generative
adversarial networks (WGAN), an improved WGAN-gradient penalty (IWGAN-GP) model
is developed to get a faster convergence speed than traditional WGAN and
generate a higher quality samples. Finally, the PPAD and IWGAN-GP models are
jointly implemented to reveal the degree of correlation between flexible loads.
Meanwhile, an intelligent offline database is built to deal with the impact of
nonlinear factors in different response scenarios. Numerical examples have been
performed with the results proving that the proposed method is significantly
better than the existing technologies in reducing load modeling deviation and
improving the responsiveness of park loads.Comment: Submitted to Expert Systems with Application
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
Integrated analysis of multi-omics data reveals T cell exhaustion in sepsis
BackgroundSepsis is a heterogeneous disease, therefore the single-gene-based biomarker is not sufficient to fully understand the disease. Higher-level biomarkers need to be explored to identify important pathways related to sepsis and evaluate their clinical significance.MethodsGene Set Enrichment Analysis (GSEA) was used to analyze the sepsis transcriptome to obtain the pathway-level expression. Limma was used to identify differentially expressed pathways. Tumor IMmune Estimation Resource (TIMER) was applied to estimate immune cell abundance. The Spearman correlation coefficient was used to find the relationships between pathways and immune cell abundance. Methylation and single-cell transcriptome data were also employed to identify important pathway genes. Log-rank test was performed to test the prognostic significance of pathways for patient survival probability. DSigDB was used to mine candidate drugs based on pathways. PyMol was used for 3-D structure visualization. LigPlot was used to plot the 2-D pose view for receptor-ligand interaction.ResultsEighty-four KEGG pathways were differentially expressed in sepsis patients compared to healthy controls. Of those, 10 pathways were associated with 28-day survival. Some pathways were significantly correlated with immune cell abundance and five pathways could be used to distinguish between systemic inflammatory response syndrome (SIRS), bacterial sepsis, and viral sepsis with Area Under the Curve (AUC) above 0.80. Seven related drugs were screened using survival-related pathways.ConclusionSepsis-related pathways can be utilized for disease subtyping, diagnosis, prognosis, and drug screening
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
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
Parallel augment Lagrangian relaxation method for transient stability constrained unit commitment
Dynamic triaxial constitutive model for rock subjected to initial stress
Building on the existing model, an improved constitutive model for rock is proposed and extended in three dimensions. The model can avoid the defect of non-zero dynamic stress at the beginning of impact loading, and the number of parameters is in a suitable range. The three-dimensional expansion method of the component combination model is similar to that of the Hooke spring, which is easy to operate and understand. For the determination of model parameters, the shared parameter estimation method based on the Levenberg–Marquardt and the Universal Global Optimization algorithm is used, which can be well applied to models with parameters that do not change with confinement and strain rates. According to the established dynamic constitutive equation, the stress–strain curve of rock under the coupling action of the initial hydrostatic pressure load and constant strain-rate impact load can be estimated theoretically. By comparing the theoretical curve with the test data, it is shown that the dynamic constitutive model is suitable for the rock under the initial pressure and impact load
Epidemic characterization and molecular genotyping of Shigella flexneri isolated from calves with diarrhea in Northwest China
Abstract Background The widespread presence of antibiotics resistance genes in pathogenic bacteria can cause enormous problems. Food animals are one of the main reservoirs of intestinal pathogens that pose a potential risk to human. Analyzing the epidemiological characteristics and resistance patterns of Shigella flexneri in calves is necessary for animal and human health. Methods and results A total of 54 Shigella flexneri isolates, including six serotypes (1a, 2a, 2b, 4a, 6 and Xv), were collected from 837 fecal samples obtained from 2014 to 2016. We performed pulsed-field gel electrophoresis (PFGE) and applied the restriction enzyme NotI to analyze the genetic relatedness among the 54 isolates and to categorize them into 31 reproducible and unique PFGE patterns. According to the results of antimicrobial susceptibility tests, all 26 Shigella flexneri 2a serotypes were resistant to cephalosporin and/or fluoroquinolones. The genes bla TEM-1 , bla OXA-1 , and bla CTX-M-14 were detected in 19 cephalosporin-resistant S. flexneri 2a isolates. Among 14 fluoroquinolone-resistant isolates, the aac(6′)-Ib-cr gene was largely present in each strain, followed by qnrS (5). Only one ciprofloxacin-resistant isolate harbored the qepA gene. Sequencing the quinolone resistance determining regions (QRDRs) of the fluoroquinolone-resistant isolates revealed two point mutations in gyrA (S83 L, D87N/Y) and a single point mutation in parC (S80I). Interestingly, two gyrA (D87N/Y) strains were resistant to ciprofloxacin. Conclusions The current study enhances our knowledge of Shigella in cattle, although continual surveillance is necessary for the control of shigellosis. The high level of cephalosporin and/or fluoroquinolone resistance in Shigella warns us of a potential risk to human and animal health
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