4 citations · 8 across the 18 of their papers we have counts for
7 papers · 2 filters
Benchmarking Self-Supervised 3D Medical Vision Foundation Models and Transferability Estimation
Jiaqi Tang, Yu Wang, Yang Liu +7
Self-supervised pretraining has expanded the pool of 3D medical foundation models, but published evaluations use different datasets, endpoints and fine-tuning protocols. Models als…
Engram Neuron Inspired Adaptive Memorization for Lifelong Learning
Suiyang Jiang, Bomin Wang, Hangqi Zhou +2
Lifelong learning enables artificial systems to adapt to changing environments. To achieve this, an effective artificial learner should dynamically reorganize past and new experien…
A generalizable structural brain MRI foundation model via dual-priority federated pretraining
Zhen Yu, Yang Liu, Jianfeng Feng +2
Foundation models hold promise for generalizable analysis of structural brain magnetic resonance imaging (MRI) scans across diverse populations and imaging conditions. However, exi…
3D Consistency Optimization for Self-Supervised Monocular Video Depth Estimation
Yuanye Liu, Ke Zhang, Junzhe Jiang +3
Reliable monocular video depth estimation is crucial for downstream 3D reasoning and embodied AI in endoscopic navigation. However, existing self-supervised approaches typically tr…
Towards 3D heart mesh generation using contactless radar imaging and physics-informed neural network
Jinye Li, Chenxi Fu, Minghang Zheng +3
Cardiac function evaluation necessitates continuous, non-invasive monitoring, a capability limited in MRI. Millimeter-wave (mmWave) radar and its Synthetic Aperture Radar (SAR) mod…
How Far Has AI Come in Liver Fibrosis Staging? A Large-Scale Real-World Dataset and Benchmark
Yuanye Liu, Nannan Shi, Zhejia Zhang +20
Despite years of methodological progress, how far AI has come in liver fibrosis staging has never been systematically evaluated under the heterogeneous, multi-center conditions tha…
X-Edit: Exact, Explicit, and Explainable Null-Space Editing for Medical Vision Transformers
Yuanye Liu, Siyuan Zhou, Ke Zhang +3
Pre-trained Vision Transformers (ViTs) are increasingly deployed for medical image classification. However, correcting their inevitable failure cases in dynamic clinical scenarios…