2 citations · 5 across the 19 of their papers we have counts for
19 papers
Radiomics--Foundation Fusion for Interpretable RCC Classification: Internal Benchmarking and Exploratory External Transfer
Yuan Liang, Fangyijie Wang, Kathleen M. Curran +3
Accurate preoperative subtype classification of renal cell carcinoma (RCC) from contrast-enhanced CT remains clinically challenging because clear cell RCC (ccRCC) and non-clear cel…
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…
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…
Cross-Modal Knowledge Distillation for PET-Free Amyloid-Beta Detection from MRI
Francesco Chiumento, Julia Dietlmeier, Ronan P. Killeen +3
Detecting amyloid- (A) positivity is crucial for early diagnosis of Alzheimer's disease but typically requires PET imaging, which is costly, invasive, and not widely accessib…
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…
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…
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…
Leveraging Information Divergence for Robust Semi-Supervised Fetal Ultrasound Image Segmentation
Fangyijie Wang, Guénolé Silvestre, Kathleen M. Curran
Maternal-fetal Ultrasound is the primary modality for monitoring fetal development, yet automated segmentation remains challenging due to the scarcity of high-quality annotations.…
Diffusion Model-based Data Augmentation Method for Fetal Head Ultrasound Segmentation
Fangyijie Wang, Kevin Whelan, Félix Balado +2
Medical image data is less accessible than in other domains due to privacy and regulatory constraints. In addition, labeling requires costly, time-intensive manual image annotation…
Fusing Radiomic Features with Deep Representations for Gestational Age Estimation in Fetal Ultrasound Images
Fangyijie Wang, Yuan Liang, Sourav Bhattacharjee +3
Accurate gestational age (GA) estimation, ideally through fetal ultrasound measurement, is a crucial aspect of providing excellent antenatal care. However, deriving GA from manual…
Semi-supervised Cervical Segmentation on Ultrasound by A Dual Framework for Neural Networks
Fangyijie Wang, Kathleen M. Curran, Guénolé Silvestre
Accurate segmentation of ultrasound (US) images of the cervical muscles is crucial for precision healthcare. The demand for automatic computer-assisted methods is high. However, th…
PSFHS Challenge Report: Pubic Symphysis and Fetal Head Segmentation from Intrapartum Ultrasound Images
Jieyun Bai, Zihao Zhou, Zhanhong Ou +26
Segmentation of the fetal and maternal structures, particularly intrapartum ultrasound imaging as advocated by the International Society of Ultrasound in Obstetrics and Gynecology…
Segmenting Fetal Head with Efficient Fine-tuning Strategies in Low-resource Settings: an empirical study with U-Net
Fangyijie Wang, Guénolé Silvestre, Kathleen M. Curran
Accurate measurement of fetal head circumference is crucial for estimating fetal growth during routine prenatal screening. Prior to measurement, it is necessary to accurately ident…
Generative Diffusion Model Bootstraps Zero-shot Classification of Fetal Ultrasound Images In Underrepresented African Populations
Fangyijie Wang, Kevin Whelan, Guénolé Silvestre +1
Developing robust deep learning models for fetal ultrasound image analysis requires comprehensive, high-quality datasets to effectively learn informative data representations withi…
Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero shot Medical Image Segmentation
Sidra Aleem, Fangyijie Wang, Mayug Maniparambil +6
The Segment Anything Model (SAM) and CLIP are remarkable vision foundation models (VFMs). SAM, a prompt driven segmentation model, excels in segmentation tasks across diverse domai…
MiTU-Net: A fine-tuned U-Net with SegFormer backbone for segmenting pubic symphysis-fetal head
Fangyijie Wang, Guenole Silvestre, Kathleen Curran
Ultrasound measurements have been examined as potential tools for predicting the likelihood of successful vaginal delivery. The angle of progression (AoP) is a measurable parameter…