We developed and validated a deep learning system (termed DeepDR Plus) in a diverse, multiethnic, multi-country dataset to predict personalized risk and time to progression of diabetic retinopathy. We show that DeepDR...
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We developed and validated a deep learning system (termed DeepDR Plus) in a diverse, multiethnic, multi-country dataset to predict personalized risk and time to progression of diabetic retinopathy. We show that DeepDR Plus can be integrated into the clinical workflow to promote individualized intervention strategies for the management of diabetic retinopathy.
Pullorum disease and fowl typhoid are caused by the Salmonella serovars Gallinarum biovars Pullorum and Gal-linarum,*** prevalence of these diseases varies across regions and is affected by different risk fac-tors tha...
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Pullorum disease and fowl typhoid are caused by the Salmonella serovars Gallinarum biovars Pullorum and Gal-linarum,*** prevalence of these diseases varies across regions and is affected by different risk fac-tors that remain insufficiently *** fill this knowledge gap,we have compiled a global dataset for its prevalence,drawing upon a collection of literature from the last seven decades obtained from bilingual ***,a more interactive and dynamic platform is still needed for both academics and policymakers to improve biosecurity measures,limit disease transmission,and prevent future outbreaks at the global and local ***,we developed an advanced visualization platform to depict the prevalence of Salmonella Pullorum and Gallinarum,espe-cially in China,which is categorized by geographical region and temporal *** platform offers a user-friendly,efficient,and visually engaging tool to explore the prevalence of pullorum disease and fowl typhoid between 1945 and 2021 in different ***,this platform allows users to understand the influence of various fac-tors,such as breed,farm mode,economic usage and even the sex of the primary host,chickens,on the prevalence of this *** further provided a detailed overview of individual province within *** particular,by selecting two different provinces on the interface,users can quickly visualize and grasp the disparities in disease prevalence between the chosen *** interactive toolkit enables a dynamic exploration of the patterns and factors con-tributing to the prevalence of Salmonella Pullorum and *** interactive platform is freely available open source at http://139.9.85.208/.
While Signed Distance Fields (SDF) are well-established for modeling watertight surfaces, Unsigned Distance Fields (UDF) broaden the scope to include open surfaces and models with complex inner structures. Despite the...
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Network distance measurement is crucial for evaluating network performance, attracting significant research attention. However, conducting measurements for the entire network is exceedingly expensive and time-consumin...
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Spatial attention mechanism has been widely incorporated into deep neural networks (DNNs), significantly lifting the performance in computer vision tasks via long-range dependency modeling. However, it may perform poo...
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Parkinson’s disease (PD) is a prevalent neurodegenerative disorder globally. The eye’s retina is an extension of the brain and has great potential in PD screening. Recent studies have suggested that texture features...
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Cortical surface registration plays a crucial role in coordinating individual cortical functions and anatomical features, serving as a fundamental step in cortical surface analysis. Its aim is to align the anatomical ...
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Cortical surface registration plays a crucial role in coordinating individual cortical functions and anatomical features, serving as a fundamental step in cortical surface analysis. Its aim is to align the anatomical or functional regions of different individuals, which is of great importance for neuroimaging studies across different populations. Currently, cortical surface registration techniques based on classical methods have been well developed. However, a key issue with classical registration methods is that for each pair of images to be registered, it is necessary to search for the optimal transformation in the deformation space according to a specific optimization algorithm until the similarity measure function converges, which cannot meet the requirements of real-time and high-precision in medical image registration. With the spectacular success of deep learning in the field of computer vision, researching cortical surface image registration techniques based on deep learning models has become a new direction. But so far, there are still only a few studies on cortical surface image registration based on deep learning. Moreover, although deep learning methods theoretically have stronger representation capabilities, surpassing the most advanced classical methods in registration accuracy and distortion control remains a challenge. Therefore, to address this challenge, this paper constructs a deep learning model to study the technology of cortical surface image registration. The specific work is as follows: (1) An unsupervised cortical surface registration network based on a multi-scale cascaded structure is designed, and a convolution method based on spherical harmonic transformation is introduced to register cortical surface data. This solves the problem of scale-inflexibility of spherical feature transformation and optimizes the multi-scale registration process. The results show that the proposed network outperforms the other deep learning-based registration m
The full array of the Large high Altitude Air Shower Observatory(LHAASO)has been in operation since July *** its kilometer-square array(KM2A),we optimized the selection criteria for very high and ultrahigh energyγ-ra...
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The full array of the Large high Altitude Air Shower Observatory(LHAASO)has been in operation since July *** its kilometer-square array(KM2A),we optimized the selection criteria for very high and ultrahigh energyγ-rays using data collected from August 2021 to August 2022,resulting in an improvement in significance of the detection in the Crab Nebula of approximately 15%,compared with that of previous *** the implementation of these new selection criteria,the angular resolution was also significantly improved by approximately 10%at tens of *** aspects of the full KM2A array performance,such as the pointing error,were also calibrated using the Crab *** resulting energy spectrum of the Crab Nebula in the energy range of 10-1000 TeV are well fitted by a log-parabola model,which is consistent with the previous results from LHAASO and other experiments.
Eosinophil detection and counting have an important role in the treatment of eosinophilic gastroenteritis. Generally, the physicians diagnose the eosinophilic gastroenteritis depending on the number of eosinophil cell...
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ISBN:
(纸本)9781665406932
Eosinophil detection and counting have an important role in the treatment of eosinophilic gastroenteritis. Generally, the physicians diagnose the eosinophilic gastroenteritis depending on the number of eosinophil cells in the corresponding pathology images. However, manually identifying eosinophils in the large pathology image is time-consuming and boring. The accuracy of Eosinophil counting significantly relies on the experiences of professional pathologists, the manual counting also prone to missing and misclassification due to fatigue. In this paper, we introduce a modified YOLOv3 object detection method to automatically count the eosinophil. Specifically, to detect the small and crowded eosinophils in the large pathology image, we carefully redesign the scale and size of candidate boxes and make our kernels more sensitive to tiny eosinophils in the feature maps. To verify our method, we collect and label a real eosinophil dataset with totally 8469 eosinophils being annotated and distributing over 255 pathology images from 11 different classes. We implemented our proposed method on our dataset and demonstrated the feasibility and bright prospect for applying object detection technique into the treatments of eosinophilic gastroenteritis.
This paper introduces OpenLS-DGF, an adaptive logic synthesis dataset generation framework, to enhance machine learning (ML) applications within the logic synthesis process. Previous dataset generation flows were tail...
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