A5032901303- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
- Medical Image Segmentation Techniques
- AI in cancer detection
- Lung Cancer Diagnosis and Treatment
- Pancreatic and Hepatic Oncology Research
- Artificial Intelligence in Healthcare and Education
- Advanced Neural Network Applications
- Water Quality Monitoring and Analysis
- Medical Imaging Techniques and Applications
- Machine Learning in Healthcare
- Hepatocellular Carcinoma Treatment and Prognosis
- Brain Tumor Detection and Classification
- Animal Nutrition and Physiology
- Natural Language Processing Techniques
- Cell Image Analysis Techniques
- MRI in cancer diagnosis
- Acute Myeloid Leukemia Research
- Medicinal Plant Pharmacodynamics Research
- Biochemical and Structural Characterization
- Topic Modeling
- Chronic Lymphocytic Leukemia Research
- Chemical Synthesis and Analysis
- Geological Modeling and Analysis
- Cancer Research and Treatments
Stanford University
2022-2025
Yancheng Teachers University
2025
Ocean University of China
2023-2025
Shandong First Medical University
2025
Hohai University
2024
Yangzhou Polytechnic Institute
2024
Qingdao National Laboratory for Marine Science and Technology
2023
Zhengzhou University
2023
Jiangsu University of Technology
2020-2022
Alibaba Group (China)
2022
With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved comparable results with inter-rater variability on many benchmark datasets. However, most existing datasets only contain single-center, single-phase, single-vendor, or single-disease cases, and it is unclear whether excellent performance can generalize diverse This paper presents large CT organ dataset, termed...
Purpose: Accurate segmentation of lung and infection in COVID-19 CT scans plays an important role the quantitative management patients. Most existing studies are based on large private annotated datasets that impractical to obtain from a single institution, especially when radiologists busy fighting coronavirus disease. Furthermore, it is hard compare current methods as they developed different datasets, trained settings, evaluated with metrics. Methods: To promote development data-efficient...
Abstract With the emergence of multidrug-resistant bacteria, antimicrobial peptides (AMPs) offer promising options for replacing traditional antibiotics to treat bacterial infections, but discovering and designing AMPs using methods is a time-consuming costly process. Deep learning has been applied de novo design address AMP classification with high efficiency. In this study, several natural language processing models were combined identify AMPs, i.e. sequence generative adversarial nets,...
Cancer-associated fibroblasts (CAFs) have been implicated in the development of resistance to anticancer drugs; however, role and mechanism underlying CAFs luminal breast cancer (BrCA) tamoxifen are unclear. We found that stromal isolated from central or peripheral area BrCA similar CAF phenotype activity. In vitro vivo experiments showed derived clinical–luminal BrCAs induce through decreasing estrogen receptor-α (ER-α) level when cultured with cell lines MCF7 T47D. promoted interleukin-6...
One hundred forty-four 25-d-old weaning piglets with BW of 6.43 ± 0.39 kg were used in a 28-d trail to evaluate the effects dietary addition spray-dried chicken plasma (SDCP) as replacement for porcine (SDPP) on growth performance, nutrient digestibility, diarrhea incidence, small intestinal morphology, digestive enzyme activity, and microflora. Pigs randomly allotted 1 4 treatments: 1) CON (control; basal diet), 2) SDPP (containing 5% SDPP), 3) + SDCP 2.5% SDCP), 4) SDCP). Six pigs from...
Quantitative organ assessment is an essential step in automated abdominal disease diagnosis and treatment planning. Artificial intelligence (AI) has shown great potential to automatize this process. However, most existing AI algorithms rely on many expert annotations lack a comprehensive evaluation of accuracy efficiency real-world multinational settings. To overcome these limitations, we organized the FLARE 2022 Challenge, largest analysis challenge date, benchmark fast, low-resource,...
0. Abstract Background The integration of large language models (LLMs) in healthcare offers immense opportunity to streamline tasks, but also carries risks such as response accuracy and bias perpetration. To address this, we conducted a red-teaming exercise assess LLMs developed dataset clinically relevant scenarios for future teams use. Methods We convened 80 multi-disciplinary experts evaluate the performance popular across multiple medical scenarios. Teams composed clinicians, engineering...
In this study, a universal protein expression enhancement RNA tool, termed RNAe, was developed by modifying recently discovered natural long non-coding RNA. At the moment, RNAe is only technology for gene enhancement, as opposed to silencing, at post-transcriptional level. With technology, an of 50–1000% achievable, with more than 200% achieved in most cases. This work identified sufficient and necessary element function, which found be merely 300 nucleotides named minRNAe. It contains 72-nt...
Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell methods are often tailored to specific modalities or require manual interventions specify hyper-parameters different experimental settings. Here, we present multi-modality benchmark, comprising over 1500 labeled images derived from more than 50 diverse biological experiments. The top participants developed Transformer-based deep-learning algorithm that not only exceeds existing but...
Conotoxins are a class of peptide toxins secreted by marine mollusks the Conus genus, characterized their unique mechanism action and significant biological activity, making them highly valuable for drug development. However, traditional methods acquiring conotoxins, such as in vivo extraction or chemical synthesis, face challenges high costs, long cycles, limited exploration sequence diversity. To address these issues, we propose ConoGPT model, conotoxin generation model that fine-tunes...
Red teaming, the practice of adversarially exposing unexpected or undesired model behaviors, is critical towards improving equity and accuracy large language models, but non-model creator-affiliated red teaming scant in healthcare. We convened teams clinicians, medical engineering students, technical professionals (80 participants total) to stress-test models with real-world clinical cases categorize inappropriate responses along axes safety, privacy, hallucinations/accuracy, bias. Six...
Addressing the challenges faced by older adults in Guangdong-Hong Kong-Macao Greater Bay Area (GHMGBA) terms of mental health and enhancing their well-being is pivotal for achieving goals cultural integration intercity connectivity. This study, grounded social exchange theory, conducted an analysis using survey data collected from 6,500 individuals (GBA). By constructing regression models, research explores impact interaction on GBA. The findings reveal that any form among GBA significantly...
<title>Abstract</title> Tourism route planning is a crucial issue that requires optimization. This article uses linear programming and sorting algorithms to screen the "Top Ten Must-Visit" destinations among 352 cities, enhancing travel efficiency representation. Innovatively, it integrates integer programming, ant colony algorithm, simulated annealing particle swarm optimization genetic graph convolutional neural network (GCN) for path planning. GCN constructs tourism maps with cities as...