2 citations · 3 across the 8 of their papers we have counts for
8 papers
Brain-Adapter: A Dual-Stream Vision-Language MIL Framework for Comprehensive 3D CT Diagnosis of Acute Intracranial Pathologies
Zhenyu Yi, Zhiyun Song, Yusong Sun +6
Automated diagnosis of 3D brain CT scans is essential for critical care, yet it remains challenging due to the heavy reliance on manual annotations and the limited semantic underst…
Unified Multimodal Model for Brain MRI Imputation and Understanding
Zhiyun Song, Che Liu, Tian Xia +2
Multimodal large language models (MLLMs) hold great potential for medicine, as they inherit knowledge from LLM and allow multiple data modalities to be integrated, analysed and int…
Brain Connectivity Network Structure Learning For Brain Disorder Diagnosis
Dongdong Chen, Linlin Yao, Mengjun Liu +7
Recent studies in neuroscience highlight the significant potential of brain connectivity networks, which are commonly constructed from functional magnetic resonance imaging (fMRI)…
Anatomy-Aware Low-Dose CT Denoising via Pretrained Vision Models and Semantic-Guided Contrastive Learning
Runze Wang, Zeli Chen, Zhiyun Song +8
To reduce radiation exposure and improve the diagnostic efficacy of low-dose computed tomography (LDCT), numerous deep learning-based denoising methods have been developed to mitig…
REHRSeg: Unleashing the Power of Self-Supervised Super-Resolution for Resource-Efficient 3D MRI Segmentation
Zhiyun Song, Yinjie Zhao, Xiaomin Li +9
High-resolution (HR) 3D magnetic resonance imaging (MRI) can provide detailed anatomical structural information, enabling precise segmentation of regions of interest for various me…
Inter-slice Super-resolution of Magnetic Resonance Images by Pre-training and Self-supervised Fine-tuning
Xin Wang, Zhiyun Song, Yitao Zhu +4
In clinical practice, 2D magnetic resonance (MR) sequences are widely adopted. While individual 2D slices can be stacked to form a 3D volume, the relatively large slice spacing can…