4 citations · 10 across the 8 of their papers we have counts for
8 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…
Sparsity-Constrained Optimal Transport
Tianlin Liu, Joan Puigcerver, Mathieu Blondel
Regularized optimal transport (OT) is now increasingly used as a loss or as a matching layer in neural networks. Entropy-regularized OT can be computed using the Sinkhorn algorithm…
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…
Finding trainable sparse networks through Neural Tangent Transfer
Tianlin Liu, Friedemann Zenke
Deep neural networks have dramatically transformed machine learning, but their memory and energy demands are substantial. The requirements of real biological neural networks are ra…
Causally Denoise Word Embeddings Using Half-Sibling Regression
Zekun Yang, Tianlin Liu
Distributional representations of words, also known as word vectors, have become crucial for modern natural language processing tasks due to their wide applications. Recently, a gr…