Jiajun Ma

BrainHGT: A Hierarchical Graph Transformer for Interpretable Brain Network Analysis

Graph Transformer shows remarkable potential in brain network analysis due to its ability to model graph structures and complex node relationships. Most existing methods typically model the brain as a flat network, ignoring its modular structure, and their attention mechanisms treat all brain region

SpReg: An Autonomous Image-To-Patient Registration Framework for Robotic Bronchoscopy

Community-Aware Graph Transformer for Brain Disorder Identification

Abnormal brain functional network is an effective biomarker for brain disease diagnosis. Most existing methods focus on mining discriminative information from whole-brain connectivity patterns. However, multi-level collaboration is the foundation of efficient brain function, in addition to the whole

Deciphering the Projection Head: Representation Evaluation Self-supervised Learning

Self-supervised learning (SSL) aims to learn the intrinsic features of data without labels. Despite the diverse SSL architectures, the projection head always plays an important role in improving downstream task performance. In this study, we systematically investigate the role of the projection head…

Elucidating the design space of classifier-guided diffusion generation

Guidance in conditional diffusion generation is of great importance for sample quality and controllability. However, existing guidance schemes are to be desired. On one hand, mainstream methods such as classifier guidance and classifier-free guidance both require extra training with labeled data,…

The Surprising Effectiveness of Skip-Tuning in Diffusion Sampling

With the incorporation of the UNet architecture, diffusion probabilistic models have become a dominant force in image generation tasks. One key design in UNet is the skip connections between the encoder and decoder blocks. Although skip connections have been shown to improve training stability and m…