369 research outputs found
TFormer: A throughout fusion transformer for multi-modal skin lesion diagnosis
Multi-modal skin lesion diagnosis (MSLD) has achieved remarkable success by
modern computer-aided diagnosis technology based on deep convolutions. However,
the information aggregation across modalities in MSLD remains challenging due
to severity unaligned spatial resolution (dermoscopic image and clinical image)
and heterogeneous data (dermoscopic image and patients' meta-data). Limited by
the intrinsic local attention, most recent MSLD pipelines using pure
convolutions struggle to capture representative features in shallow layers,
thus the fusion across different modalities is usually done at the end of the
pipelines, even at the last layer, leading to an insufficient information
aggregation. To tackle the issue, we introduce a pure transformer-based method,
which we refer to as ``Throughout Fusion Transformer (TFormer)", for sufficient
information intergration in MSLD. Different from the existing approaches with
convolutions, the proposed network leverages transformer as feature extraction
backbone, bringing more representative shallow features. We then carefully
design a stack of dual-branch hierarchical multi-modal transformer (HMT) blocks
to fuse information across different image modalities in a stage-by-stage way.
With the aggregated information of image modalities, a multi-modal transformer
post-fusion (MTP) block is designed to integrate features across image and
non-image data. Such a strategy that information of the image modalities is
firstly fused then the heterogeneous ones enables us to better divide and
conquer the two major challenges while ensuring inter-modality dynamics are
effectively modeled. Experiments conducted on the public Derm7pt dataset
validate the superiority of the proposed method. Our TFormer outperforms other
state-of-the-art methods. Ablation experiments also suggest the effectiveness
of our designs
A Rotation Meanout Network with Invariance for Dermoscopy Image Classification and Retrieval
The computer-aided diagnosis (CAD) system can provide a reference basis for
the clinical diagnosis of skin diseases. Convolutional neural networks (CNNs)
can not only extract visual elements such as colors and shapes but also
semantic features. As such they have made great improvements in many tasks of
dermoscopy images. The imaging of dermoscopy has no principal orientation,
indicating that there are a large number of skin lesion rotations in the
datasets. However, CNNs lack rotation invariance, which is bound to affect the
robustness of CNNs against rotations. To tackle this issue, we propose a
rotation meanout (RM) network to extract rotation-invariant features from
dermoscopy images. In RM, each set of rotated feature maps corresponds to a set
of outputs of the weight-sharing convolutions and they are fused using meanout
strategy to obtain the final feature maps. Through theoretical derivation, the
proposed RM network is rotation-equivariant and can extract rotation-invariant
features when followed by the global average pooling (GAP) operation. The
extracted rotation-invariant features can better represent the original data in
classification and retrieval tasks for dermoscopy images. The RM is a general
operation, which does not change the network structure or increase any
parameter, and can be flexibly embedded in any part of CNNs. Extensive
experiments are conducted on a dermoscopy image dataset. The results show our
method outperforms other anti-rotation methods and achieves great improvements
in dermoscopy image classification and retrieval tasks, indicating the
potential of rotation invariance in the field of dermoscopy images
Identification and validation of a pyroptosis-related signature in identifying active tuberculosis via a deep learning algorithm
IntroductionActive tuberculosis (ATB), instigated by Mycobacterium tuberculosis (M.tb), rises as a primary instigator of morbidity and mortality within the realm of infectious illnesses. A significant portion of M.tb infections maintain an asymptomatic nature, recognizably termed as latent tuberculosis infections (LTBI). The complexities inherent to its diagnosis significantly hamper the initiatives aimed at its control and eventual eradication.MethodologyUtilizing the Gene Expression Omnibus (GEO), we procured two dedicated microarray datasets, labeled GSE39940 and GSE37250. The technique of weighted correlation network analysis was employed to discern the co-expression modules from the differentially expressed genes derived from the first dataset, GSE39940. Consequently, a pyroptosis-related module was garnered, facilitating the identification of a pyroptosis-related signature (PRS) diagnostic model through the application of a neural network algorithm. With the aid of Single Sample Gene Set Enrichment Analysis (ssGSEA), we further examined the immune cells engaged in the pyroptosis process in the context of active ATB. Lastly, dataset GSE37250 played a crucial role as a validating cohort, aimed at evaluating the diagnostic prowess of our model.ResultsIn executing the Weighted Gene Co-expression Network Analysis (WGCNA), a total of nine discrete co-expression modules were lucidly elucidated. Module 1 demonstrated a potent correlation with pyroptosis. A predictive diagnostic paradigm comprising three pyroptosis-related signatures, specifically AIM2, CASP8, and NAIP, was devised accordingly. The established PRS model exhibited outstanding accuracy across both cohorts, with the area under the curve (AUC) being respectively articulated as 0.946 and 0.787.ConclusionThe present research succeeded in identifying the pyroptosis-related signature within the pathogenetic framework of ATB. Furthermore, we developed a diagnostic model which exuded a remarkable potential for efficient and accurate diagnosis
Spatiotemporal patterns of particulate matter (PM) and associations between PM and mortality in Shenzhen, China
DOI: 10.1186/s12889-016-2725-6Background: Most studies on air pollution exposure and its associations with human health in China have focused on
the heavily polluted industrial areas and/or mega-cities, and studies on cities with comparatively low air pollutant
concentrations are still rare. Only a few studies have attempted to analyse particulate matter (PM) for the vibrant
economic centre Shenzhen in the Pearl River Delta. So far no systematic investigation of PM spatiotemporal patterns
in Shenzhen has been undertaken and the understanding of pollution exposure in urban agglomerations with
comparatively low pollution is still limited.
Methods: We analyze daily and hourly particulate matter concentrations and all-cause mortality during 2013 in
Shenzhen, China. Temporal patterns of PM (PM2.5 and PM10) with aerodynamic diameters of 2.5 (10) ÎĽm or less
(or less (including particles with a diameter that equals to 2.5 (10) ÎĽm) are studied, along with the ratio of PM2.5 to
PM10. Spatial distributions of PM10 and PM2.5 are addressed and associations of PM10 or PM2.5 and all-cause mortality
are analyzed.
Results: Annual average PM10 and PM2.5 concentrations were 61.3 and 39.6 ÎĽg/m3 in 2013. PM2.5 failed to meet the
Class 2 annual limit of the National Ambient Air Quality Standard. PM2.5 was the primary air pollutant, with 8.8 % of
days having heavy PM2.5 pollution. The daily PM2.5/PM10 ratios were high. Hourly PM2.5 concentrations in the tourist
area were lower than downtown throughout the day. PM10 and PM2.5 concentrations were higher in western parts of
Shenzhen than in eastern parts. Excess risks in the number of all-cause mortality with a 10 ÎĽg/m3 increase of PM were
0.61 % (95 % confidence interval [CI]: 0.50–0.72) for PM10, and 0.69 % (95 % CI: 0.55–0.83) for PM2.5, respectively. The
greatest ERs of PM10 and PM2.5 were in 2-day cumulative measures for the all-cause mortality, 2-day lag for females and
the young (0–65 years), and L02 for males and the elder (>65 years). PM2.5 had higher risks on all-cause mortality than
PM10. Effects of high PM pollution on mortality were stronger in the elder and male.
Conclusions: Our findings provide additional relevant information on air quality monitoring and associations of PM
and human health, valuable data for further scientific research in Shenzhen and for the on-going discourse on
improving environmental policie
Efficient refinements on YOLOv3 for real-time detection and assessment of diabetic foot Wagner grades
Currently, the screening of Wagner grades of diabetic feet (DF) still relies
on professional podiatrists. However, in less-developed countries, podiatrists
are scarce, which led to the majority of undiagnosed patients. In this study,
we proposed the real-time detection and location method for Wagner grades of DF
based on refinements on YOLOv3. We collected 2,688 data samples and implemented
several methods, such as a visual coherent image mixup, label smoothing, and
training scheduler revamping, based on the ablation study. The experimental
results suggested that the refinements on YOLOv3 achieved an accuracy of 91.95%
and the inference speed of a single picture reaches 31ms with the NVIDIA Tesla
V100. To test the performance of the model on a smartphone, we deployed the
refinements on YOLOv3 models on an Android 9 system smartphone. This work has
the potential to lead to a paradigm shift for clinical treatment of the DF in
the future, to provide an effective healthcare solution for DF tissue analysis
and healing status.Comment: 11 pages with 11 figure
Transgender sexual health in China: a cross-sectional online survey in China
Transgender individuals are at increased risk for HIV infection around the world, yet few studies have focused on transgender individuals in China. We conducted an online cross-sectional survey of men who have sex with men (MSM) and transgender individuals to examine sociodemographics, intimate partner violence (IPV) and sexual behaviours in China
Two-level mixed-effects height to crown base model for moso bamboo (Phyllostachys edulis) in Eastern China
Height to crown base (HCB) is an important predictor variable for forest growth and yield models and is of great significance for bamboo stem utilization. However, existing HCB models built so far on the hierarchically structured data are for arbor forests, and not applied to bamboo forests. Based on the fitting of data acquired from 38 temporary sample plots of Phyllostachys edulis forests in Yixing, Jiangsu Province, we selected the best HCB model (logistic model) from among six basic models and extended it by integrating predictor variables, which involved evaluating the impact of 13 variables on HCB. Block- and sample plot-level random effects were introduced to the extended model to account for nested data structures through mixed-effects modeling. The results showed that bamboo height, diameter at breast height, total basal area of all bamboo individuals with a diameter larger than that of the subject bamboo, and canopy density contributed significantly more to variation in HCB than other variables did. Introducing two-level random effects resulted in a significant improvement in the accuracy of the model. Different sampling strategies were evaluated for response calibration (model localization), and the optimal strategy was identified. The prediction accuracy of the HCB model was substantially improved, with an increase in the number of bamboo samples in the calibration. Based on our findings, we recommend the use of four randomly selected bamboo individuals per sample to provide a compromise between measurement cost, model use efficiency, and prediction accuracy
Determination of cinnamic acid in human urine by flow injection chemiluminescence
It was found that cinnamic acid can react with potassium permanganate in the acidic medium and produce chemiluminescence, which was greatly enhanced by glyoxal. Under the optimum conditions, the linear range for the determination of cinnamic acid was 1.0Ă—10-8 to 1.0Ă—10-4 mol L-1 with a detection limit of 8.0Ă—10-9 mol L-1, the relative standard deviation was 1.7% for 2.0Ă—10-6 mol L-1 cinnamic acid solution in nine repeated measurements. This method was found to be novel0simple0fast and sensitive, it was successfully applied to the determination of cinnamic acid in human urine. Furthermore, the possible reaction mechanism was also discussed
- …