7 papers
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…
Multi-Scale Transformer Architecture for Accurate Medical Image Classification
Jiacheng Hu, Yanlin Xiang, Yang Lin +3
This study introduces an AI-driven skin lesion classification algorithm built on an enhanced Transformer architecture, addressing the challenges of accuracy and robustness in medic…
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…