2 citations · 3 across the 4 of their papers we have counts for
4 papers
Understanding the Transfer Limits of Vision Foundation Models
Shiqi Huang, Yipei Wang, Natasha Thorley +8
Foundation models leverage large-scale pretraining to capture extensive knowledge, demonstrating generalization in a wide range of language tasks. By comparison, vision foundation…
Scaling Artificial Intelligence for Prostate Cancer Detection on MRI towards Organized Screening and Primary Diagnosis in a Global, Multiethnic Population (Study Protocol)
Anindo Saha, Joeran S. Bosma, Jasper J. Twilt +26
In this intercontinental, confirmatory study, we include a retrospective cohort of 22,481 MRI examinations (21,288 patients; 46 cities in 22 countries) to train and externally vali…
Impact of Clinical Image Quality on Efficient Foundation Model Finetuning
Yucheng Tang, Pawel Rajwa, Alexander Ng +11
Foundation models in medical imaging have shown promising label efficiency, achieving high performance on downstream tasks using only a fraction of the annotated data otherwise req…
T2-Only Prostate Cancer Prediction by Meta-Learning from Bi-Parametric MR Imaging
Weixi Yi, Yipei Wang, Natasha Thorley +6
Current imaging-based prostate cancer diagnosis requires both MR T2-weighted (T2w) and diffusion-weighted imaging (DWI) sequences, with additional sequences for potentially greater…