2 citations · 5 across the 8 of their papers we have counts for
4 papers · 1 filter
FlexICL: A Flexible Visual In-context Learning Framework for Elbow and Wrist Ultrasound Segmentation
Yuyue Zhou, Jessica Knight, Shrimanti Ghosh +3
Elbow and wrist fractures are the most common fractures in pediatric populations. Automatic segmentation of musculoskeletal structures in ultrasound (US) can improve diagnostic acc…
Time-Contrastive Pretraining for In-Context Image and Video Segmentation
Assefa Wahd, Jacob Jaremko, Abhilash Hareendranathan
In-context learning (ICL) enables generalization to new tasks with minimal labeled data. However, mainstream ICL approaches rely on a gridding strategy, which lacks the flexibility…
Sam2Rad: A Segmentation Model for Medical Images with Learnable Prompts
Assefa Seyoum Wahd, Banafshe Felfeliyan, Yuyue Zhou +5
Foundation models like the segment anything model require high-quality manual prompts for medical image segmentation, which is time-consuming and requires expertise. SAM and its va…
Sample Efficient Learning of Image-Based Diagnostic Classifiers Using Probabilistic Labels
Roberto Vega, Pouneh Gorji, Zichen Zhang +5
Deep learning approaches often require huge datasets to achieve good generalization. This complicates its use in tasks like image-based medical diagnosis, where the small training…