10 citations · 26 across the 19 of their papers we have counts for
20 papers
A Deep Learning Framework for Boundary-Aware Semantic Segmentation
Tai An, Weiqiang Huang, Da Xu +3
As a fundamental task in computer vision, semantic segmentation is widely applied in fields such as autonomous driving, remote sensing image analysis, and medical image processing.…
Adaptive Transformer Attention and Multi-Scale Fusion for Spine 3D Segmentation
Yanlin Xiang, Qingyuan He, Ting Xu +3
This study proposes a 3D semantic segmentation method for the spine based on the improved SwinUNETR to improve segmentation accuracy and robustness. Aiming at the complex anatomica…
A Hybrid CNN-Transformer Model for Heart Disease Prediction Using Life History Data
Ran Hao, Yanlin Xiang, Junliang Du +3
This study proposed a hybrid model of a convolutional neural network (CNN) and a Transformer to predict and diagnose heart disease. Based on CNN's strength in detecting local featu…
A Deep Learning Approach to Interface Color Quality Assessment in HCI
Shixiao Wang, Runsheng Zhang, Junliang Du +2
In this paper, a quantitative evaluation model for the color quality of human-computer interaction interfaces is proposed by combining deep convolutional neural networks (CNN). By…
Contrastive Learning for Cold Start Recommendation with Adaptive Feature Fusion
Jiacheng Hu, Tai An, Zidong Yu +2
This paper proposes a cold start recommendation model that integrates contrastive learning, aiming to solve the problem of performance degradation of recommendation systems in cold…
A Structured Reasoning Framework for Unbalanced Data Classification Using Probabilistic Models
Junliang Du, Shiyu Dou, Bohuan Yang +2
This paper studies a Markov network model for unbalanced data, aiming to solve the problems of classification bias and insufficient minority class recognition ability of traditiona…
Deep Learning in Image Classification: Evaluating VGG19's Performance on Complex Visual Data
Weijie He, Tong Zhou, Yanlin Xiang +3
This study aims to explore the automatic classification method of pneumonia X-ray images based on VGG19 deep convolutional neural network, and evaluate its application effect in pn…
Accurate Medical Named Entity Recognition Through Specialized NLP Models
Jiacheng Hu, Runyuan Bao, Yang Lin +2
This study evaluated the effect of BioBERT in medical text processing for the task of medical named entity recognition. Through comparative experiments with models such as BERT, Cl…
Enhancing Medical Image Segmentation with Deep Learning and Diffusion Models
Houze Liu, Tong Zhou, Yanlin Xiang +3
Medical image segmentation is crucial for accurate clinical diagnoses, yet it faces challenges such as low contrast between lesions and normal tissues, unclear boundaries, and high…
Few-Shot Learning with Adaptive Weight Masking in Conditional GANs
Jiacheng Hu, Zhen Qi, Jianjun Wei +3
Deep learning has revolutionized various fields, yet its efficacy is hindered by overfitting and the requirement of extensive annotated data, particularly in few-shot learning scen…
Enhancing Recommendation Systems with GNNs and Addressing Over-Smoothing
Wenyi Liu, Ziqi Zhang, Xinshi Li +3
This paper addresses key challenges in enhancing recommendation systems by leveraging Graph Neural Networks (GNNs) and addressing inherent limitations such as over-smoothing, which…
Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Data
Aoran Shen, Minghao Dai, Jiacheng Hu +3
In the 21st-century information age, with the development of big data technology, effectively extracting valuable information from massive data has become a key issue. Traditional…
Enhancing Few-Shot Learning with Integrated Data and GAN Model Approaches
Yinqiu Feng, Aoran Shen, Jiacheng Hu +3
This paper presents an innovative approach to enhancing few-shot learning by integrating data augmentation with model fine-tuning in a framework designed to tackle the challenges p…
Deep Learning with HM-VGG: AI Strategies for Multi-modal Image Analysis
Junliang Du, Yiru Cang, Tong Zhou +2
This study introduces the Hybrid Multi-modal VGG (HM-VGG) model, a cutting-edge deep learning approach for the early diagnosis of glaucoma. The HM-VGG model utilizes an attention m…
Deep Learning for Medical Text Processing: BERT Model Fine-Tuning and Comparative Study
Jiacheng Hu, Yiru Cang, Guiran Liu +3
This paper proposes a medical literature summary generation method based on the BERT model to address the challenges brought by the current explosion of medical information. By fin…
Optimizing Retrieval-Augmented Generation with Elasticsearch for Enhanced Question-Answering Systems
Jiajing Chen, Runyuan Bao, Hongye Zheng +3
This study aims to improve the accuracy and quality of large-scale language models (LLMs) in answering questions by integrating Elasticsearch into the Retrieval Augmented Generatio…
Optimizing YOLOv5s Object Detection through Knowledge Distillation algorithm
Guanming Huang, Aoran Shen, Yuxiang Hu +3
This paper explores the application of knowledge distillation technology in target detection tasks, especially the impact of different distillation temperatures on the performance…
Research on Dynamic Data Flow Anomaly Detection based on Machine Learning
Liyang Wang, Yu Cheng, Hao Gong +3
The sophistication and diversity of contemporary cyberattacks have rendered the use of proxies, gateways, firewalls, and encrypted tunnels as a standalone defensive strategy inadeq…
Deep Learning-Based Channel Squeeze U-Structure for Lung Nodule Detection and Segmentation
Mingxiu Sui, Jiacheng Hu, Tong Zhou +3
This paper introduces a novel deep-learning method for the automatic detection and segmentation of lung nodules, aimed at advancing the accuracy of early-stage lung cancer diagnosi…
Axial Attention Transformer Networks: A New Frontier in Breast Cancer Detection
Weijie He, Runyuan Bao, Yiru Cang +3
This paper delves into the challenges and advancements in the field of medical image segmentation, particularly focusing on breast cancer diagnosis. The authors propose a novel Tra…