3 citations · 8 across the 11 of their papers we have counts for
6 papers · 1 filter
MG-3D: Multi-Grained Knowledge-Enhanced 3D Medical Vision-Language Pre-training
Xuefeng Ni, Linshan Wu, Jiaxin Zhuang +6
3D medical image analysis is pivotal in numerous clinical applications. However, the scarcity of labeled data and limited generalization capabilities hinder the advancement of AI-e…
Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?
Pedro R. A. S. Bassi, Wenxuan Li, Yucheng Tang +50
How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified…
Large-Scale 3D Medical Image Pre-training with Geometric Context Priors
Linshan Wu, Jiaxin Zhuang, Hao Chen
The scarcity of annotations poses a significant challenge in medical image analysis. Large-scale pre-training has emerged as a promising label-efficient solution, owing to the util…
FreeTumor: Advance Tumor Segmentation via Large-Scale Tumor Synthesis
Linshan Wu, Jiaxin Zhuang, Xuefeng Ni +1
AI-driven tumor analysis has garnered increasing attention in healthcare. However, its progress is significantly hindered by the lack of annotated tumor cases, which requires radio…
MiM: Mask in Mask Self-Supervised Pre-Training for 3D Medical Image Analysis
Jiaxin Zhuang, Linshan Wu, Qiong Wang +4
The Vision Transformer (ViT) has demonstrated remarkable performance in Self-Supervised Learning (SSL) for 3D medical image analysis. Masked AutoEncoder (MAE) for feature pre-train…
VoCo: A Simple-yet-Effective Volume Contrastive Learning Framework for 3D Medical Image Analysis
Linshan Wu, Jiaxin Zhuang, Hao Chen
Self-Supervised Learning (SSL) has demonstrated promising results in 3D medical image analysis. However, the lack of high-level semantics in pre-training still heavily hinders the…