Xiuyuan Xu

Orcid: 0000-0002-2505-4350

According to our database1, Xiuyuan Xu authored at least 25 papers between 2018 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

On csauthors.net:

Bibliography

2026
Box-guided class-contextual representation learning for self-visual lung nodule detection.
Biomed. Signal Process. Control., 2026

Mitigating Entity Hallucinations in 3D Radiology Report Generation via Dual-Stream Alignment.
Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

GeoCoBox: Box-supervised 3D Tumor Segmentation via Geometric Co-embedding.
Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

RoSE: A Role Correlation Structure-Enhanced Model for Multi-Event Argument Extraction.
Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

2025
SA-Seg: Annotation-Efficient Segmentation for Airway Tree Using Saliency-Based Annotation.
IEEE Trans. Medical Imaging, November, 2025

Learning from certain regions of interest in medical images via probabilistic positive-unlabeled networks.
Medical Image Anal., 2025

LooBox: Loose-box-supervised 3D Tumor Segmentation with Self-correcting Bidirectional Learning.
Proceedings of the 33rd ACM International Conference on Multimedia, 2025

Domain Generalization for Pulmonary Nodule Detection via Distributionally-Regularized Mamba.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2025, 2025

2024
Segmentation of Lung Lesions through Bilateral Learning Branches to Aggregating Contextual and Local Characteristics.
Int. J. Comput. Intell. Syst., December, 2024

A Forward Learning Algorithm for Neural Memory Ordinary Differential Equations.
Int. J. Neural Syst., September, 2024

ICNoduleNet: Enhancing Pulmonary Nodule Detection Performance on Sharp Kernel CT Imaging.
IEEE J. Biomed. Health Informatics, August, 2024

Efficient and Gender-Adaptive Graph Vision Mamba for Pediatric Bone Age Assessment.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2024, 2024

2023
A scale-aware UNet++ model combined with attentional context supervision and adaptive Tversky loss for accurate airway segmentation.
Appl. Intell., August, 2023

The Gap in the Thickness: Estimating Effectiveness of Pulmonary Nodule Detection in Thick- and Thin-Section CT Images with 3D Deep Neural Networks.
Comput. Methods Programs Biomed., February, 2023

Multi-Label Softmax Networks for Pulmonary Nodule Classification Using Unbalanced and Dependent Categories.
IEEE Trans. Medical Imaging, 2023

2022
Automatic Diagnose of Drug-Resistance Tuberculosis from CT Images Based on Deep Neural Networks.
Proceedings of the Artificial Intelligence - Second CAAI International Conference, 2022

2021
Automatic airway tree segmentation based on multi-scale context information.
Int. J. Comput. Assist. Radiol. Surg., 2021

RPLS-Net: pulmonary lobe segmentation based on 3D fully convolutional networks and multi-task learning.
Int. J. Comput. Assist. Radiol. Surg., 2021

Application of Internet Plus: TCM Clinical Intelligent Decision Making.
Proceedings of the Advances in Swarm Intelligence - 12th International Conference, 2021

2020
MediMLP: Using Grad-CAM to Extract Crucial Variables for Lung Cancer Postoperative Complication Prediction.
IEEE J. Biomed. Health Informatics, 2020

MSCS-DeepLN: Evaluating lung nodule malignancy using multi-scale cost-sensitive neural networks.
Medical Image Anal., 2020

DeepLN: A framework for automatic lung nodule detection using multi-resolution CT screening images.
Knowl. Based Syst., 2020

2019
DeepLNAnno: a Web-Based Lung Nodules Annotating System for CT Images.
J. Medical Syst., 2019

Multi-task Learning for the Segmentation of Thoracic Organs at Risk in CT images.
Proceedings of the 2019 Challenge on Segmentation of THoracic Organs at Risk in CT Images, 2019

2018
DeepCXray: Automatically Diagnosing Diseases on Chest X-Rays Using Deep Neural Networks.
IEEE Access, 2018


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