42 citations · 68 across the 16 of their papers we have counts for
17 papers
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…
BEAM-Net: A Deep Learning Framework with Bone Enhancement Attention Mechanism for High Resolution High Frame Rate Ultrasound Beamforming
Midhila Madhusoodanan, Mahesh Raveendranatha Panicker, Pisharody Harikrishnan Gopalakrishnan +1
Pocket-sized, low-cost point-of-care ultrasound (POCUS) devices are increasingly used in musculoskeletal (MSK) applications for structural examination of bone tissue. However, the…
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…
Reinforcement Learning for Ultrasound Image Analysis A Comprehensive Review of Advances and Applications
Maha Ezzelarab, Midhila Madhusoodanan, Shrimanti Ghosh +3
Over the last decade, the use of machine learning (ML) approaches in medicinal applications has increased manifold. Most of these approaches are based on deep learning, which aims…
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…
A Simple Framework Uniting Visual In-context Learning with Masked Image Modeling to Improve Ultrasound Segmentation
Yuyue Zhou, Banafshe Felfeliyan, Shrimanti Ghosh +6
Conventional deep learning models deal with images one-by-one, requiring costly and time-consuming expert labeling in the field of medical imaging, and domain-specific restriction…