Xiaohong Liu

Orcid: 0000-0002-0818-1059

Affiliations:
  • Tsinghua University, Beijing, China


According to our database1, Xiaohong Liu authored at least 25 papers between 2019 and 2024.

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

Timeline

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Bibliography

2024
Dense Contrastive-Based Federated Learning for Dense Prediction Tasks on Medical Images.
IEEE J. Biomed. Health Informatics, April, 2024

Enhancing High-Resolution Image Compression Through Local-Global Joint Attention Mechanism.
IEEE Signal Process. Lett., 2024

CoPRA: Bridging Cross-domain Pretrained Sequence Models with Complex Structures for Protein-RNA Binding Affinity Prediction.
CoRR, 2024

SA-FedLora: Adaptive Parameter Allocation for Efficient Federated Learning with LoRA Tuning.
CoRR, 2024

A Comprehensive Dataset and Automated Pipeline for Nailfold Capillary Analysis.
Proceedings of the IEEE International Symposium on Biomedical Imaging, 2024

2023
Improving artificial intelligence pipeline for liver malignancy diagnosis using ultrasound images and video frames.
Briefings Bioinform., January, 2023

Domain Knowledge Driven Semantic Communication for Image Transmission Over Wireless Channels.
IEEE Wirel. Commun. Lett., 2023

MedKPL: A heterogeneous knowledge enhanced prompt learning framework for transferable diagnosis.
J. Biomed. Informatics, 2023

A Comprehensive Dataset and Automated Pipeline for Nailfold Capillary Analysis.
CoRR, 2023

TCM-GPT: Efficient Pre-training of Large Language Models for Domain Adaptation in Traditional Chinese Medicine.
CoRR, 2023

Self Adaptive Global-Local Feature Enhancement for Radiology Report Generation.
Proceedings of the IEEE International Conference on Image Processing, 2023

TAMM: A Task-Adaptive Multi-Modal Fusion Network for Facial-Related Health Assessments on 3D Facial Images.
Proceedings of the IEEE International Conference on Image Processing, 2023

FEDMBP: Multi-Branch Prototype Federated Learning on Heterogeneous Data.
Proceedings of the IEEE International Conference on Image Processing, 2023

Enhancing Medical Language Understanding: Adapting LLMs to the Medical Domain through Hybrid Granularity Mask Learning.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2023

2022
Explainable Dynamic Multimodal Variational Autoencoder for the Prediction of Patients With Suspected Central Precocious Puberty.
IEEE J. Biomed. Health Informatics, 2022

Yolo-SG: Salience-Guided Detection Of Small Objects In Medical Images.
Proceedings of the 2022 IEEE International Conference on Image Processing, 2022

AIAT: Adaptive Iteration Adversarial Training for Robust Pulmonary Nodule Detection.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2022

Enhanced CT Image Generation by GAN for Improving Thyroid Anatomy Detection.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2022

AMAT-Net: An Unbiased Network with High Performance for Metabolic Diseases Prediction Using Facial Images.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2022

Semantic Reasoning with NLI for Assertion Detection in Medical Text.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2022

2021
Deep Active Learning For Fibrosis Segmentation Of Chest CT Scans From Covid-19 Patients.
Proceedings of the 2021 IEEE International Conference on Image Processing, 2021

2020
Anterior Segment Eye Lesion Segmentation with Advanced Fusion Strategies and Auxiliary Tasks.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020

KISEG: A Three-Stage Segmentation Framework for Multi-level Acceleration of Chest CT Scans from COVID-19 Patients.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020

2019
DeepTriager: A Neural Attention Model for Emergency Triage with Electronic Health Records.
Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine, 2019

Bone Age Assessment by Deep Convolutional Neural Networks Combined with Clinical TW3-RUS.
Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine, 2019


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