5 citations · 8 across the 6 of their papers we have counts for
6 papers
Context-driven Missing-Modality Learning for Robust Medical Diagnosis with Image-Tabular Data
Tianling Liu, Lequan Yu, Tong Han +1
While multimodal data integrating diverse imaging and clinical tabular records is crucial for accurate medical diagnosis, the arbitrary absence of specific modalities is prevalent…
CFCML: A Coarse-to-Fine Crossmodal Learning Framework For Disease Diagnosis Using Multimodal Images and Tabular Data
Tianling Liu, Hongying Liu, Fanhua Shang +3
In clinical practice, crossmodal information including medical images and tabular data is essential for disease diagnosis. There exists a significant modality gap between these dat…
Completed Feature Disentanglement Learning for Multimodal MRIs Analysis
Tianling Liu, Hongying Liu, Fanhua Shang +3
Multimodal MRIs play a crucial role in clinical diagnosis and treatment. Feature disentanglement (FD)-based methods, aiming at learning superior feature representations for multimo…
Joint Prediction of Meningioma Grade and Brain Invasion via Task-Aware Contrastive Learning
Tianling Liu, Wennan Liu, Lequan Yu +3
Preoperative and noninvasive prediction of the meningioma grade is important in clinical practice, as it directly influences the clinical decision making. What's more, brain invasi…
NestedFormer: Nested Modality-Aware Transformer for Brain Tumor Segmentation
Zhaohu Xing, Lequan Yu, Liang Wan +2
Multi-modal MR imaging is routinely used in clinical practice to diagnose and investigate brain tumors by providing rich complementary information. Previous multi-modal MRI segment…
Networked Sensing in 6G Cellular Networks: Opportunities and Challenges
Liang Liu, Shuowen Zhang, Rui Du +2
Radar and wireless communication are widely acknowledged as the two most successful applications of the radio technology over the past decades. Recently, there is a trend in both a…