5 papers
Robust Cross-Domain Generalization Using Unlabeled Target Data with Source-Domain Supervision
Yuyue Zhou, Shrimanti Ghosh, Michael +7
It is often desirable to generalize medical imaging AI models trained with dense annotations to data acquired from different ultrasound scanners or clinical sites; however, retrain…
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…
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…
Self-supervised TransUNet for Ultrasound regional segmentation of the distal radius in children
Yuyue Zhou, Jessica Knight, Banafshe Felfeliyan +3
Supervised deep learning offers great promise to automate analysis of medical images from segmentation to diagnosis. However, their performance highly relies on the quality and qua…
Physics Driven Domain Specific Transporter Framework with Attention Mechanism for Ultrasound Imaging
Arpan Tripathi, Abhilash Rakkunedeth, Mahesh Raveendranatha Panicker +7
Most applications of deep learning techniques in medical imaging are supervised and require a large number of labeled data which is expensive and requires many hours of careful ann…