A5114833544- AI in cancer detection
- Radiomics and Machine Learning in Medical Imaging
- Digital Imaging for Blood Diseases
- Colorectal Cancer Screening and Detection
- Image Enhancement Techniques
- Biomedical Text Mining and Ontologies
- Video Surveillance and Tracking Methods
- Advanced Image Processing Techniques
- Structural Engineering and Vibration Analysis
- Allergic Rhinitis and Sensitization
- Autonomous Vehicle Technology and Safety
- Advanced Image Fusion Techniques
- Advanced Text Analysis Techniques
- Sentiment Analysis and Opinion Mining
- Inflammatory mediators and NSAID effects
- Radio Wave Propagation Studies
- Direction-of-Arrival Estimation Techniques
- Pediatric health and respiratory diseases
- Synthesis of β-Lactam Compounds
- Transportation Safety and Impact Analysis
- Neurogenetic and Muscular Disorders Research
- Wireless Signal Modulation Classification
- Advanced Optical Imaging Technologies
- Advanced Measurement and Metrology Techniques
- Artificial Intelligence in Healthcare
Anhui University
2024
Pennsylvania State University
2021-2023
University of Würzburg
2023
Jiangsu Vocational College of Medicine
2022
Xi'an Jiaotong University
2018-2021
Southeast University
2019
Dalian Polytechnic University
2014
Guangdong Medical College
2012
ShenZhen People’s Hospital
2012
This work reviews the results of NTIRE 2021 Challenge on Non-Homogeneous Dehazing. The proposed techniques and their have been evaluated a novel dataset that extends NH-Haze datset. It consists additional 35 pairs real haze free nonhomogeneous hazy images recorded outdoor. has introduced in outdoor scenes by using professional setup imitates conditions scenes. 327 participants registered challenge 23 teams competed final testing phase. solutions gauge state-of-the-art image dehazing.
We present the new Bokeh Effect Transformation Dataset (BETD), and review proposed solutions for this novel task at NTIRE 2023 Challenge. Recent advancements of mobile photography aim to reach visual quality full-frame cameras. Now, a goal in computational is optimize effect itself, which aesthetic blur out-of-focus areas an image. Photographers create by benefiting from lens optical properties.The work design neural network capable converting one another without harming sharp foreground...
Image dehazing is one of the most challenging imaging inverse problems that estimates haze-free images from hazy ones. While recent transformer/convolutional neural network-based methods have shown excellent performance in handling both homogeneous and non-homogeneous problems, these networks are often trained end-to-end to estimate image directly require a large number parameters. In this work, we propose novel, lightweight two-stage deep network for dehazing. particular, our proposed...
In this paper, a two-step approach for vehicles detection is proposed.The first step of to approximate vehicles' potential locations through searching shadow area vehicle low-part.In order find these shadows, Haar-like feature with Adaboost was used train Haar detector offline and the relearning process hard training samples applied increase rate.Based on previous processing, ROI (Region interest) + HOG SVM algorithm verification.At last, K-means combine similar results.The experimental...
Noninvasive prenatal diagnosis (NIPD) of single-gene disorders has recently become the focus clinical laboratories. However, reports on application NIPD Duchenne muscular dystrophy (DMD) are limited. This study aimed to evaluate detection performance haplotype-based DMD in a real environment. Twenty-one families at 7-12 weeks gestation were prospectively recruited. DNA libraries cell-free from pregnant and genomic family members captured using custom assay for enrichment gene exons spanning...
Histopathological images carry informative cellular visual phenotypes and have been digitalized in huge amount medical institutes. However, the lack of software for annotating specialized has a hurdle fully exploiting educating researching, enabling intelligent systems automatic diagnosis or phenotype-genotype association study. This paper proposes an open-source web framework, OpenHI, whole-slide image annotation. The proposed framework could be utilized simultaneous collaborative...
In recent years, algorithm unrolling has emerged as a powerful technique for designing interpretable neural networks based on iterative algorithms. Imaging inverse problems have particularly benefited from deep network design since many traditional model-based approaches rely optimization. Despite exciting progress, typical heuristically layer-specific convolution weights to improve performance. Crucially, convergence properties of the underlying are lost once layer specific parameters...
Diagnostic pathology, which is the basis and gold standard of cancer diagnosis, provides essential information on prognosis disease vital evidence for clinical treatment. However, pathological diagnosis subjective, differences in observation between pathologists are common. This phenomenon more evident hospitals with insufficient medical resources. Deep learning (DL) can be used to identify classify structures digital pathology. In order solve above difficulties, this work, we propose a DL...
Consolidating semantically rich annotation on digital histopathological images known as whole-slide requires a software capable of handling such type biomedical data with support for procedures which align existing pathological protocols. Demands large-scale annotated datasets are the raise since they needed developments artificial intelligence techniques to promote automated diagnosis, mass screening, phenotype-genotype association study, etc. This paper presents an open platform efficient...
Pathological is crucial to cancer diagnosis. Usually, Pathologists draw their conclusion based on observed cell and tissue structure histology slides. Rapid development in machine learning, especially deep learning have established robust accurate classifiers. They are being used analyze histopathological slides assist pathologists Most systems rely heavily annotated data sets gain experiences knowledge correctly accurately perform various tasks such as classification segmentation....
Digital pathology plays a crucial role in the development of artificial intelligence medical field. The digital platform can make pathological resources and networked, realize permanent storage visual data synchronous browsing processing without limitation time space. It has been widely used various fields pathology. However, there is still lack an open universal to assist doctors management analysis sections, as well structured description relevant patient information. Most platforms cannot...
Transition from conventional to digital pathology requires a new category of biomedical informatic infrastructure which could facilitate delicate pathological routine. Pathological diagnoses are sensitive many external factors and is known be subjective. Only systems that can meet strict requirements in would able run along routines eventually digitized the area, developed platform should comply with existing international standards. Currently, there number available software tools perform...
Personalized diagnoses have not been possible due to a sear amount of data pathologists bear during the day-to-day routine, leading current generalized standards being continuously updated as new findings are reported. It is noticeable that these practical developed based on multi-source heterogeneous data, including whole-slide images and pathology clinical reports. In this study, we propose framework combines pathological medical reports generate personalized diagnosis result for an...
Existing audio-visual cross-modal matching methods focus on mitigating heterogeneity but ignore the impact of intra-class discrepancy same identity in different scenarios, which might greatly limit performance. To simultaneously handle both problems and heterogeneity, we propose a novel public-private attributes-based variational adversarial network ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> <sup...
The grade of clear cell renal carcinoma (ccRCC) is a critical prognostic factor, making ccRCC nuclei grading crucial task in RCC pathology analysis. Computer-aided aims to improve pathologists' work efficiency while reducing their misdiagnosis rate by automatically identifying the grades tumor within histopathological images. Such requires precisely segment and accurately classify nuclei. However, most existing segmentation classification methods can not handle inter-class similarity...
Diagnostic pathology, which is the basis and gold standard of cancer diagnosis, provides essential information on prognosis disease vital evidence for clinical treatment. Tumor region detection, subtype grade classification are fundamental diagnostic indicators renal cell carcinoma (RCC) in whole-slide images (WSIs). However, pathological diagnosis subjective, differences observation between pathologists common hospitals with inadequate capacity. The main challenge developing deep learning...
Personalized diagnoses have not been possible due to sear amount of data pathologists bear during the day-to-day routine. This lead current generalized standards that are being continuously updated as new findings reported. It is noticeable these effective developed based on a multi-source heterogeneous data, including whole-slide images and pathology clinical reports. In this study, we propose framework combines pathological medical reports generate personalized diagnosis result for...
Taking the steel canopy project of main stage Chengdu Open-air Music Square as research object, analyzes construction difficulty, tight schedule and high quality requirements from aspects difficulties, installation methods process; puts forward reasonable structure division steps; at same time, innovatively large-scale inverted "7 At proposes a large "7" truss lifting method an anti-deformation its for hyperbolic mesh shell structure; fully utilizes simulation analysis, monitoring testing...
To achieve very high data rates in 3-D multilayer optical storage systems, a novel approach is investigated to read out parallel multiple tracks at different layers simultaneously. Data bits are arranged as titled pages inside the disk. A uniform beam sheet generated illuminate desired page from top of disk, and depth transfer imaging system used collect fluorescence written within detector array. The performance illumination optics has been experimentally evaluated optimized by aberration...