J. Alison Noble
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View article: FedAgentBench: Towards Automating Real-world Federated Medical Image Analysis with Server-Client LLM Agents
FedAgentBench: Towards Automating Real-world Federated Medical Image Analysis with Server-Client LLM Agents Open
Federated learning (FL) allows collaborative model training across healthcare sites without sharing sensitive patient data. However, real-world FL deployment is often hindered by complex operational challenges that demand substantial human…
View article: Latent Motion Profiling for Annotation-Free Cardiac Phase Detection in Adult and Fetal Echocardiography Videos
Latent Motion Profiling for Annotation-Free Cardiac Phase Detection in Adult and Fetal Echocardiography Videos Open
View article: Neural Collapse-Inspired Multi-Label Federated Learning under Label-Distribution Skew
Neural Collapse-Inspired Multi-Label Federated Learning under Label-Distribution Skew Open
Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy, yet it remains challenging as data distributions can be highly heterogeneous. These challenges are further amplified in …
View article: IterMask3D: Unsupervised anomaly detection and segmentation with test-time iterative mask refinement in 3D brain MRI
IterMask3D: Unsupervised anomaly detection and segmentation with test-time iterative mask refinement in 3D brain MRI Open
Unsupervised anomaly detection and segmentation methods train a model to learn the training distribution as 'normal'. In the testing phase, they identify patterns that deviate from this normal distribution as 'anomalies'. To learn the 'nor…
View article: HarmonicEchoNet: Leveraging harmonic convolutions for automated standard plane detection in fetal heart ultrasound videos
HarmonicEchoNet: Leveraging harmonic convolutions for automated standard plane detection in fetal heart ultrasound videos Open
Fetal echocardiography offers non-invasive and real-time imaging acquisition of fetal heart images to identify congenital heart conditions. Manual acquisition of standard heart views is time-consuming, whereas automated detection remains c…
View article: Machine learning-based method for the detection of dextrocardia in ultrasound video clips
Machine learning-based method for the detection of dextrocardia in ultrasound video clips Open
Our automated method demonstrates accurate segmentation and reliable detection of dextrocardia from US videos. Due to the simple acquisition protocol and its robust analytical pipeline, our method is suitable for healthcare providers who a…
View article: Latent Motion Profiling for Annotation-free Cardiac Phase Detection in Adult and Fetal Echocardiography Videos
Latent Motion Profiling for Annotation-free Cardiac Phase Detection in Adult and Fetal Echocardiography Videos Open
The identification of cardiac phase is an essential step for analysis and diagnosis of cardiac function. Automatic methods, especially data-driven methods for cardiac phase detection, typically require extensive annotations, which is time-…
View article: Artificial Intelligence Image-Diagnosis for Female Genital Schistosomiasis
Artificial Intelligence Image-Diagnosis for Female Genital Schistosomiasis Open
This study highlights the potential of deep learning-based models for automated diagnosis for FGS while reducing the reliance on specialized clinical expertise. It also underscores the need for further work to address current limitations o…
View article: Self-supervised Learning of Echocardiographic Video Representations via Online Cluster Distillation
Self-supervised Learning of Echocardiographic Video Representations via Online Cluster Distillation Open
Self-supervised learning (SSL) has achieved major advances in natural images and video understanding, but challenges remain in domains like echocardiography (heart ultrasound) due to subtle anatomical structures, complex temporal dynamics,…
View article: Prenatal detection of congenital heart defects using the deep learning-based image and video analysis: protocol for Clinical Artificial Intelligence in Fetal Echocardiography (CAIFE), an international multicentre multidisciplinary study
Prenatal detection of congenital heart defects using the deep learning-based image and video analysis: protocol for Clinical Artificial Intelligence in Fetal Echocardiography (CAIFE), an international multicentre multidisciplinary study Open
Introduction Congenital heart defect (CHD) is a significant, rapidly emerging global problem in child health and a leading cause of neonatal and childhood death. Prenatal detection of CHDs with the help of ultrasound allows better perinata…
View article: Artificial intelligence-enabled prenatal ultrasound for the detection of fetal cardiac abnormalities: a systematic review and meta-analysis
Artificial intelligence-enabled prenatal ultrasound for the detection of fetal cardiac abnormalities: a systematic review and meta-analysis Open
View article: Are you simulator ready? A study of maritime communication competence in the full-mission bridge simulator
Are you simulator ready? A study of maritime communication competence in the full-mission bridge simulator Open
The term literacy is not used simply to denote whether an individual is literate or illiterate. The term has greater implications. Thus literacy, as a social practice, {“is what people do with reading, writing and texts in real world conte…
View article: ScanAhead: Simplifying standard plane acquisition of fetal head ultrasound
ScanAhead: Simplifying standard plane acquisition of fetal head ultrasound Open
The fetal standard plane acquisition task aims to detect an Ultrasound (US) image characterized by specified anatomical landmarks and appearance for assessing fetal growth. However, in practice, due to variability in human operator skill a…
View article: Extent of Microlearning Habit and Students' Vocabulary Retention
Extent of Microlearning Habit and Students' Vocabulary Retention Open
This study investigates the degree of microlearning practices and how they affect vocabulary retention in West Visayas State University-Himamaylan City Campus BSEd English majors. A questionnaire and vocabulary retention test created by th…
View article: PREgnancy Care Integrating translational Science, Everywhere (PRECISE): a prospective cohort study of African pregnant and non-pregnant women to investigate placental disorders – cohort profile
PREgnancy Care Integrating translational Science, Everywhere (PRECISE): a prospective cohort study of African pregnant and non-pregnant women to investigate placental disorders – cohort profile Open
Purpose The PREgnancy Care Integrating translational Science, Everywhere Network was established to investigate specific placental disorders (pregnancy hypertension, preterm birth, fetal growth restriction and stillbirth) in sub-Saharan Af…
View article: FedPIA – Permuting and Integrating Adapters Leveraging Wasserstein Barycenters for Finetuning Foundation Models in Multi-Modal Federated Learning
FedPIA – Permuting and Integrating Adapters Leveraging Wasserstein Barycenters for Finetuning Foundation Models in Multi-Modal Federated Learning Open
Large Vision-Language Models (VLMs), possessing millions or billions of parameters, typically require large text and image datasets for effective fine-tuning. However, collecting data from various sites, especially in healthcare, is challe…
View article: Incongruent Multimodal Federated Learning for Medical Vision and Language-based Multi-label Disease Detection
Incongruent Multimodal Federated Learning for Medical Vision and Language-based Multi-label Disease Detection Open
Federated Learning (FL) in healthcare ensures patient privacy by allowing hospitals to collaboratively train machine learning models while keeping sensitive medical data secure and localized. Most existing research in FL has concentrated o…
View article: Trustworthy and Practical AI for Healthcare: A Guided Deferral System with Large Language Models
Trustworthy and Practical AI for Healthcare: A Guided Deferral System with Large Language Models Open
Large language models (LLMs) offer a valuable technology for various applications in healthcare. However, their tendency to hallucinate and the existing reliance on proprietary systems pose challenges in environments concerning critical de…
View article: MCAT: Visual Query-Based Localization of Standard Anatomical Clips in Fetal Ultrasound Videos Using Multi-Tier Class-Aware Token Transformer
MCAT: Visual Query-Based Localization of Standard Anatomical Clips in Fetal Ultrasound Videos Using Multi-Tier Class-Aware Token Transformer Open
Accurate standard plane acquisition in fetal ultrasound (US) videos is crucial for fetal growth assessment, anomaly detection, and adherence to clinical guidelines. However, manually selecting standard frames is time-consuming and prone to…
View article: Self-supervised Normality Learning and Divergence Vector-guided Model Merging for Zero-shot Congenital Heart Disease Detection in Fetal Ultrasound Videos
Self-supervised Normality Learning and Divergence Vector-guided Model Merging for Zero-shot Congenital Heart Disease Detection in Fetal Ultrasound Videos Open
Congenital Heart Disease (CHD) is one of the leading causes of fetal mortality, yet the scarcity of labeled CHD data and strict privacy regulations surrounding fetal ultrasound (US) imaging present significant challenges for the developmen…
View article: Expert-Agnostic Learning to Defer
Expert-Agnostic Learning to Defer Open
Learning to Defer (L2D) trains autonomous systems to handle straightforward cases while deferring uncertain ones to human experts. Recent advancements in this field have introduced methods that offer flexibility to unseen experts at test t…
View article: A comprehensive scoping review on machine learning-based fetal echocardiography analysis
A comprehensive scoping review on machine learning-based fetal echocardiography analysis Open
Fetal echocardiography (ultrasound of the fetal heart) plays a vital role in identifying heart defects, allowing clinicians to establish prenatal and postnatal management plans. Machine learning-based methods are emerging to support the au…
View article: Fedexit - Missing Class-Agnostic Semi-Supervised Federated Learning with Extreme Imbalance Tackling Scheme
Fedexit - Missing Class-Agnostic Semi-Supervised Federated Learning with Extreme Imbalance Tackling Scheme Open
View article: FedPIA -- Permuting and Integrating Adapters leveraging Wasserstein Barycenters for Finetuning Foundation Models in Multi-Modal Federated Learning
FedPIA -- Permuting and Integrating Adapters leveraging Wasserstein Barycenters for Finetuning Foundation Models in Multi-Modal Federated Learning Open
Large Vision-Language Models typically require large text and image datasets for effective fine-tuning. However, collecting data from various sites, especially in healthcare, is challenging due to strict privacy regulations. An alternative…
View article: Detection of fetal congenital heart defects on three-vessel view ultrasound videos
Detection of fetal congenital heart defects on three-vessel view ultrasound videos Open
Background:: Detecting congenital heart defects (CHDs) is challenging due to the difficulty of identifying subtle abnormalities in fetal heart structures. Objectives:: To develop a deep learning-based method for segmenting vessels in the t…
View article: SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation
SPA: Efficient User-Preference Alignment against Uncertainty in Medical Image Segmentation Open
Medical image segmentation data inherently contain uncertainty. This can stem from both imperfect image quality and variability in labeling preferences on ambiguous pixels, which depend on annotator expertise and the clinical context of th…
View article: F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics
F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Open
Effective training of large Vision-Language Models (VLMs) on resource-constrained client devices in Federated Learning (FL) requires the usage of parameter-efficient fine-tuning (PEFT) strategies. To this end, we demonstrate the impact of …
View article: Portable ultrasound devices for obstetric care in resource-constrained environments: mapping the landscape
Portable ultrasound devices for obstetric care in resource-constrained environments: mapping the landscape Open
Background The WHO’s recommendations on antenatal care underscore the need for ultrasound assessment during pregnancy. Given that maternal and perinatal mortality remains unacceptably high in underserved regions, these guidelines are imper…
View article: Prospective randomized evaluation of the sustained impact of assistive artificial intelligence on anesthetists’ ultrasound scanning for regional anesthesia
Prospective randomized evaluation of the sustained impact of assistive artificial intelligence on anesthetists’ ultrasound scanning for regional anesthesia Open
Objectives Ultrasound-guided regional anesthesia (UGRA) relies on acquiring and interpreting an appropriate view of sonoanatomy. Artificial intelligence (AI) has the potential to aid this by applying a color overlay to key sonoanatomical s…
View article: A physics-based ensemble machine-learning approach to identifying a relationship between lightning indices and binary lightning hazard
A physics-based ensemble machine-learning approach to identifying a relationship between lightning indices and binary lightning hazard Open
To convert lightning indices generated by numerical weather prediction experiments into binary lightning hazard, a machine-learning tool was developed. This tool, consisting of parallel multilayer perceptron classifiers, was trained on an …