4 citations · 8 across the 13 of their papers we have counts for
6 papers · 1 filter
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…
Topology-Driven Transferability Estimation for 3D Medical Vision Foundation Models
Jiaqi Tang, Shaoyang Zhang, Fandong Zhang +3
The growing number of medical vision foundation models highlights the need for effective model selection. However, mainstream selection methods rely on exhaustive fine-tuning, whic…
TriDP-PTM: a three-stage distortion-perception tradeoff guides the pre-training model for radar cardiac sensing
Jinye Li, Aidong Men, Yang Liu +1
Cardiovascular diseases (CVDs) remain a leading cause of death globally, necessitating continuous, accurate non-invasive cardiac monitoring. While non-contact radar-based approache…
3D MRI Image Pretraining via Controllable 2D Slice Navigation Task
Yu Wang, Qingchao Chen
Self-supervised pretraining has become the mainstream approach for learning MRI representations from unlabeled scans. However, most existing objectives still treat each scan primar…
The Texture-Shape Dilemma: Boundary-Safe Synthetic Generation for 3D Medical Transformers
Jiaqi Tang, Weixuan Xu, Shu Zhang +2
Vision Transformers (ViTs) have revolutionized medical image analysis, yet their data-hungry nature clashes with the scarcity and privacy constraints of clinical archives. Formula-…
Fake It Right: Injecting Anatomical Logic into Synthetic Supervised Pre-training for Medical Segmentation
Jiaqi Tang, Mengyan Zheng, Shu Zhang +2
Vision Transformers (ViTs) excel in 3D medical segmentation but require massive annotated datasets. While Self-Supervised Learning (SSL) mitigates this using unlabeled data, it sti…