6 papers
No Modality Left Behind: Adapting to Missing Modalities via Knowledge Distillation for Brain Tumor Segmentation
Shenghao Zhu, Yifei Chen, Weihong Chen +6
Accurate brain tumor segmentation is essential for preoperative evaluation and personalized treatment. Multi-modal MRI is widely used due to its ability to capture complementary tu…
GLIDE: Graph-guided Leap Inference for Diffusion Estimation of Spatio-Temporal Point Processes
Guanyu Zhou, Yao Liu, Yanglei Gan +5
Spatio-temporal point processes (STPPs) provide a principled framework for modeling asynchronous events in continuous time and space. Recent diffusion-based approaches offer a flex…
JLT: Clean-Latent Prediction in Latent Diffusion Transformers
Funing Fu, Tenghui Wang, Guanyu Zhou +2
Flow matching with clean-data prediction has shown that regressing the clean point can exploit low-dimensional structure more effectively than predicting an ambient noised quantity…
DGSAN: Dual-Graph Spatiotemporal Attention Network for Pulmonary Nodule Malignancy Prediction
Xiao Yu, Zhaojie Fang, Guanyu Zhou +7
Lung cancer continues to be the leading cause of cancer-related deaths globally. Early detection and diagnosis of pulmonary nodules are essential for improving patient survival rat…
CSF-Net: Cross-Modal Spatiotemporal Fusion Network for Pulmonary Nodule Malignancy Predicting
Yin Shen, Zhaojie Fang, Ke Zhuang +8
Pulmonary nodules are an early sign of lung cancer, and detecting them early is vital for improving patient survival rates. Most current methods use only single Computed Tomography…
TC-KANRecon: High-Quality and Accelerated MRI Reconstruction via Adaptive KAN Mechanisms and Intelligent Feature Scaling
Ruiquan Ge, Xiao Yu, Yifei Chen +10
Magnetic Resonance Imaging (MRI) has become essential in clinical diagnosis due to its high resolution and multiple contrast mechanisms. However, the relatively long acquisition ti…