13 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…
DyABD: The Abdominal Muscle Segmentation in Dynamic MRI Benchmark
Niamh Belton, Victoria Joppin, Aonghus Lawlor +4
This work introduces DyABD, a novel and complex benchmark dataset of dynamic abdominal MRIs from patients with abdominal hernias and associated high quality abdominal muscle annota…
Are Natural-Domain Foundation Models Effective for Accelerated Cardiac MRI Reconstruction?
Anam Hashmi, Mayug Maniparambil, Julia Dietlmeier +2
The emergence of large-scale pretrained foundation models has transformed computer vision, enabling strong performance across diverse downstream tasks. However, their potential for…