4 citations · 11 across the 7 of their papers we have counts for
7 papers
Generalizable Cervical Cancer Screening via Large-scale Pretraining and Test-Time Adaptation
Hao Jiang, Cheng Jin, Huangjing Lin +15
Cervical cancer is a leading malignancy in female reproductive system. While AI-assisted cytology offers a cost-effective and non-invasive screening solution, current systems strug…
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…
Surgformer: Surgical Transformer with Hierarchical Temporal Attention for Surgical Phase Recognition
Shu Yang, Luyang Luo, Qiong Wang +1
Existing state-of-the-art methods for surgical phase recognition either rely on the extraction of spatial-temporal features at a short-range temporal resolution or adopt the sequen…
A Large Model for Non-invasive and Personalized Management of Breast Cancer from Multiparametric MRI
Luyang Luo, Mingxiang Wu, Mei Li +8
Breast Magnetic Resonance Imaging (MRI) demonstrates the highest sensitivity for breast cancer detection among imaging modalities and is standard practice for high-risk women. Inte…
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…
GSCo: Towards Generalizable AI in Medicine via Generalist-Specialist Collaboration
Sunan He, Yuxiang Nie, Hongmei Wang +21
Generalist foundation models (GFMs) are renowned for their exceptional capability and flexibility in effectively generalizing across diverse tasks and modalities. In the field of m…
Learning Robust Medical Image Segmentation from Multi-source Annotations
Yifeng Wang, Luyang Luo, Mingxiang Wu +2
Collecting annotations from multiple independent sources could mitigate the impact of potential noises and biases from a single source, which is a common practice in medical image…