4 citations · 10 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
Learning Multiscale Convolutional Dictionaries for Image Reconstruction
Tianlin Liu, Anadi Chaman, David Belius +1
Convolutional neural networks (CNNs) have been tremendously successful in solving imaging inverse problems. To understand their success, an effective strategy is to construct simpl…