A5038504786- AI in cancer detection
- Radiomics and Machine Learning in Medical Imaging
- Digital Imaging for Blood Diseases
- Cervical Cancer and HPV Research
- Advanced Neural Network Applications
- Regulation of Appetite and Obesity
- Reproductive Physiology in Livestock
- Domain Adaptation and Few-Shot Learning
- Cancer Genomics and Diagnostics
- Adipose Tissue and Metabolism
- Brain Tumor Detection and Classification
- Circadian rhythm and melatonin
- Fire Detection and Safety Systems
- Cell Image Analysis Techniques
- Adipokines, Inflammation, and Metabolic Diseases
- Underwater Vehicles and Communication Systems
- Machine Fault Diagnosis Techniques
- Gene expression and cancer classification
- Target Tracking and Data Fusion in Sensor Networks
- Control and Dynamics of Mobile Robots
- Plant Disease Management Techniques
- Advanced Algorithms and Applications
- Cancer Immunotherapy and Biomarkers
- Infrared Target Detection Methodologies
- Prostate Cancer Diagnosis and Treatment
Northwest A&F University
2019-2025
Wuhan University
2017-2024
Guangxi University of Chinese Medicine
2024
Chinese Academy of Fishery Sciences
2023
Harbin Engineering University
2011-2023
National Astronomical Observatories
2023
Northeast Petroleum University
2023
Institute of Art
2023
Guangzhou University
2022
Kunming University of Science and Technology
2010-2020
Alternaria leaf spot, Brown Mosaic, Grey and Rust are five common types of apple diseases that severely affect yield. However, the existing research lacks an accurate fast detector for ensuring healthy development industry. This paper proposes a deep learning approach is based on improved convolutional neural networks (CNNs) real-time detection diseases. In this paper, disease dataset (ALDD), which composed laboratory images complex under real field conditions, first constructed via data...
Accurate and reliable perception systems are essential for autonomous driving robotics. To achieve this, 3D object detection with multi-sensors is necessary. Existing detectors have significantly improved accuracy by adopting a two-stage paradigm that relies solely on LiDAR point clouds proposal refinement. However, the sparsity of clouds, particularly faraway points, makes it difficult LiDAR-only refinement module to recognize locate objects accurately. address this issue, we propose novel...
Abstract Reliable fault diagnosis for wind turbine main shaft bearings faces dual challenges: existing deep learning models demand excessive computational resources industrial deployment, and weak fault-induced acoustic emission signals under low-speed/heavy-load conditions are prone to noise interference, thereby degrading diagnostic reliability. To address these challenges, we propose SEDSNet, a lightweight residual network bearing diagnosis. SEDSNet integrates depthwise separable...
With the worldwide carbon neutralization boom, low-speed heavy load bearings have been widely used in field of wind power. Bearing failure generates impulses when rolling element passes cracked surface bearing. Over past decade, acoustic emission (AE) techniques to detect signals. However, high sampling rates AE signals make it difficult design and extract fault features; thus, deep neural network-based approaches proposed. In this paper, we proposed an improved RepVGG bearing diagnosis...
Unmanned Aerial Vehicles (UAVs) play an important role in applications such as data collection and target reconnaissance. An accurate optimal path can effectively increase the mission success rate case of small UAVs. Although planning for UAVs is similar to that traditional mobile robots, special kinematic characteristics (such their minimum turning radius) have not been taken into account previous studies. In this paper, we propose a locally-adjustable, continuous-curvature, bounded...
Abstract Background Circulating metabolites (CM) play a pivotal role in our overall health, yet the current evidence concerning involvement of diverse CM benign prostatic hyperplasia (BPH) remains limited. Mendelian randomization (MR) offers promising avenue to explore potential impact on BPH. Methods In forward MR analysis, cohort 249 circulating was employed as exposures investigate their associations with BPH risk. Conversely, reverse an exposure assess its effects CM. Results The...
Sound speed profiles (SSPs) have a great impact on the accuracy of underwater localization and sonar ranging. In traditional SSP inversion, sound intensity distribution used in normal mode theory-based matching field processing (MFP) or multipath signal propagation time adopted ray MFP is susceptible to boundary parameter mismatch issues, which reduces inversion accuracy. Moreover, heuristic algorithms introduced require many individuals iterations search for optimal feature representation...
Effects of melatonin on the release and synthesis gonadotropin-releasing hormone (GnRH) luteinizing (LH) at hypothalamus pituitary levels have been explored in some species, but a similar study corpora lutea (CL) has not yet conducted. In this study, immunostaining for GnRH LH was observed luteal cells porcine CL during pregnancy, significant effect pregnant stage level found; higher values mRNA were detected early mid-stages than later-stage (P < 0.01). Furthermore, patterns membrane...
Breast cancer histopathological image classification has made great progress with the use of Convolutional Neural Networks (CNNs). However, due to limited receptive field, CNNs have difficulty in learning global information breast images, hindering further improvement this task. To solve problem, we reasonably apply self-attention mechanism task and propose a new network called Local-Global Vision Transformer (LGViT) which utilizes capture local features learn images. LGViT several...
Abstract Cervical cancer is one of the most common cancers in daily life. Early detection and diagnosis can effectively help facilitate subsequent clinical treatment management. With growing advancement artificial intelligence (AI) deep learning (DL) techniques, an increasing number computer-aided (CAD) methods based on have been applied cervical cytology screening. In this paper, we survey more than 70 publications since 2016 to provide a systematic comprehensive review DL-based First,...
Fine-needle aspiration cytology (FNAC) is regarded as one of the most important preoperative diagnostic tests for thyroid nodules. However, traditional process FNAC time-consuming, and its accuracy highly related to experience cytopathologist. Computer-aided (CAD) systems are rapidly evolving provide objective recommendations. So far, studies have used fixed-size patches usually hand-select model training. In this study, we develop a CAD system address these challenges. order be consistent...