10 papers
Efficient Ultrasound Image Segmentation with Token-Conditioned Neural Cellular Automata
Fangyijie Wang, Tanya Akumu, Zi Ye +3
Point-of-Care Ultrasound (POCUS) plays an important role in bedside diagnosis and clinical decision-making, particularly in resource-constrained settings. Recent deep learning meth…
Dual Agreement Consistency Learning for Semi-Supervised Fetal Ultrasound Segmentation
Fangyijie Wang, Guénolé Silvestre, Ziyang Wang +1
Maternal-fetal US is the primary imaging modality for monitoring fetal development, yet accurate automated segmentation remains challenging due to the scarcity of pixel-level annot…
A Clinician-Centered Pipeline for Annotation and Evaluation in Ultrasound AI Studies
Fangyijie Wang, Jianjun Yu, Wentao Shi +4
Clinician-centered evaluation is critical for validating medical AI systems, especially in ultrasound imaging where quantitative metrics do not always capture clinical usability. E…
Understanding Task Aggregation for Generalizable Ultrasound Foundation Models
Fangyijie Wang, Tanya Akumu, Vien Ngoc Dang +5
Foundation models promise to unify multiple clinical tasks within a single framework, but recent ultrasound studies report that unified models can underperform task-specific baseli…
Dual Agreement Consistency Learning with Foundation Models for Semi-Supervised Fetal Heart Ultrasound Segmentation and Diagnosis
Fangyijie Wang, Guénolé Silvestre, Kathleen M. Curran
Congenital heart disease (CHD) screening from fetal echocardiography requires accurate analysis of multiple standard cardiac views, yet developing reliable artificial intelligence…
Entropy-Guided Agreement-Diversity: A Semi-Supervised Active Learning Framework for Fetal Head Segmentation in Ultrasound
Fangyijie Wang, Siteng Ma, Guénolé Silvestre +1
Fetal ultrasound (US) data is often limited due to privacy and regulatory restrictions, posing challenges for training deep learning (DL) models. While semi-supervised learning (SS…