Julia Alison Noble

Julia Alison Noble
University of Oxford | OX · Department of Engineering Science

Doctor of Philosophy

About

593
Publications
86,986
Reads
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15,379
Citations
Introduction
Prof. Noble's research interests are in computational (machine-learning based) analysis of images and their healthcare application. Her group is best known for its work in ultrasound image analysis, including on clinical needs in HIC and LMIC settings.

Publications

Publications (593)
Chapter
Ultrasound (US)-probe motion estimation is a fundamental problem in automated standard plane locating during obstetric US diagnosis. Most recent existing recent works employ deep neural network (DNN) to regress the probe motion. However, these deep regression-based methods leverage the DNN to overfit on the specific training data, which is naturall...
Chapter
Video quality assurance is an important topic in obstetric ultrasound imaging to ensure that captured videos are suitable for biometry and fetal health assessment. Previously, one successful objective approach to automated ultrasound image quality assurance has considered it as a supervised learning task of detecting anatomical structures defined b...
Chapter
Self-knowledge distillation (SKD) is a recent and promising machine learning approach where a shallow student network is trained to distill its own knowledge. By contrast, in traditional knowledge distillation a student model distills its knowledge from a large teacher network model, which involves vast computational complexity and a large storage...
Preprint
The prowess that makes few-shot learning desirable in medical image analysis is the efficient use of the support image data, which are labelled to classify or segment new classes, a task that otherwise requires substantially more training images and expert annotations. This work describes a fully 3D prototypical few-shot segmentation algorithm, suc...
Article
Full-text available
Background: Pre-eclampsia is a leading cause of maternal mortality and morbidity that involves pregnancy-related stressors on the maternal cardiovascular and metabolic systems. As nutrition is important to support optimal development of the placenta and for the developing fetus, maternal diets may play a role in preventing pre-eclampsia. The purpo...
Article
Background The World Health Organization recommends a package of pregnancy care that includes obstetric ultrasound scans. There are significant barriers to universal access to antenatal ultrasound, particularly because of the cost and need for maintenance of ultrasound equipment and a lack of trained personnel. As low-cost, handheld ultrasound devi...
Chapter
Eye trackers can provide visual guidance to sonographers during ultrasound (US) scanning. Such guidance is potentially valuable for less experienced operators to improve their scanning skills on how to manipulate the probe to achieve the desired plane. In this paper, a multimodal guidance approach (Multimodal-GuideNet) is proposed to capture the st...
Article
In this work, we present a novel gaze-assisted natural language processing (NLP)-based video captioning model to describe routine second-trimester fetal ultrasound scan videos in a vocabulary of spoken sonography. The primary novelty of our multi-modal approach is that the learned video captioning model is built using a combination of ultrasound vi...
Preprint
Full-text available
Self-supervised contrastive representation learning offers the advantage of learning meaningful visual representations from unlabeled medical datasets for transfer learning. However, applying current contrastive learning approaches to medical data without considering its domain-specific anatomical characteristics may lead to visual representations...
Preprint
Eye trackers can provide visual guidance to sonographers during ultrasound (US) scanning. Such guidance is potentially valuable for less experienced operators to improve their scanning skills on how to manipulate the probe to achieve the desired plane. In this paper, a multimodal guidance approach (Multimodal-GuideNet) is proposed to capture the st...
Conference Paper
Ultrasound(US)-probe motion estimation is a fundamental problem in automated standard plane locating during obstetric US diagnosis. Most recent existing recent works employ deep neural network(DNN) to regress the probe motion. However, these deep regression-based methods leverage the DNN to overfit on the specific training data, which is naturally...
Article
Full-text available
Purpose For highly operator-dependent ultrasound scanning, skill assessment approaches evaluate operator competence given available data, such as acquired images and tracked probe movement. Operator skill level can be quantified by the completeness, speed, and precision of performing a clinical task, such as biometry. Such clinical tasks are increa...
Conference Paper
This study presents a novel approach to automatic detection and segmentation of the Crown Rump Length (CRL) and Nuchal Translucency (NT), two essential measurements in the first trimester US scan. The proposed method automatically localises a standard plane within a video clip as defined by the UK Fetal Abnormality Screening Programme. A Nested Hou...
Preprint
Full-text available
In this paper, we consider image quality assessment (IQA) as a measure of how images are amenable with respect to a given downstream task, or task amenability. When the task is performed using machine learning algorithms, such as a neural-network-based task predictor for image classification or segmentation, the performance of the task predictor pr...
Article
SlowflowHD is a new ultrasound Doppler imaging technology that allows visualization of flow within small blood vessels. In this mode, a proprietary algorithm differentiates between low-speed flow and signals attributed to tissue motion so that microvessel vasculature can be examined. Our objectives were to describe the low-velocity Doppler mode pri...
Article
Full-text available
In this paper, we consider image quality assessment (IQA) as a measure of how images are amenable with respect to a given downstream task, or task amenability. When the task is performed using machine learning algorithms, such as a neural-network-based task predictor for image classification or segmentation, the performance of the task predictor pr...
Preprint
BACKGROUND Ultrasound for gestational age (GA) assessment is not routinely available in resource-constrained settings, particularly in rural and remote locations. The TraCer device combines a handheld wireless ultrasound probe and a tablet with Artificial Intelligence (AI)-enabled software that obtains GA from videos of the fetal head by automated...
Chapter
We present a method for classifying tasks in fetal ultrasound scans using the eye-tracking data of sonographers. The visual attention of a sonographer captured by eye-tracking data over time is defined by a scanpath. In routine fetal ultrasound, the captured standard imaging planes are visually inconsistent due to fetal position, movements, and son...
Chapter
Deep networks have been shown to achieve impressive accuracy for some medical image analysis tasks where large datasets and annotations are available. However, tasks involving learning over new sets of classes arriving over extended time is a different and difficult challenge due to the tendency of reduction in performance over old classes while ad...
Chapter
The performance of many medical image analysis tasks are strongly associated with image data quality. When developing modern deep learning algorithms, rather than relying on subjective (human-based) image quality assessment (IQA), task amenability potentially provides an objective measure of task-specific image quality. To predict task amenability,...
Chapter
Automated ultrasound (US)-probe movement guidance is desirable to assist inexperienced human operators during obstetric US scanning. In this paper, we present a new visual-assisted probe movement technique using automated landmark retrieval for assistive obstetric US scanning. In a first step, a set of landmarks is constructed uniformly around a vi...
Conference Paper
The performance of many medical image analysis tasks are strongly associated with image data quality. When developing modern deep learning algorithms, rather than relying on subjective (human-based) image quality assessment (IQA), task amenability potentially provides an objective measure of task-specific image quality. To predict task amenability,...
Preprint
Full-text available
Fetal alcohol syndrome (FAS) caused by prenatal alcohol exposure can result in a series of cranio-facial anomalies, and behavioral and neurocognitive problems. Current diagnosis of FAS is typically done by identifying a set of facial characteristics, which are often obtained by manual examination. Anatomical landmark detection, which provides rich...
Preprint
Full-text available
This paper presents a novel approach to automatic fetal brain biometry motivated by needs in low- and medium- income countries. Specifically, we leverage high-end (HE) ultrasound images to build a biometry solution for low-cost (LC) point-of-care ultrasound images. We propose a novel unsupervised domain adaptation approach to train deep models to b...
Preprint
The performance of many medical image analysis tasks are strongly associated with image data quality. When developing modern deep learning algorithms, rather than relying on subjective (human-based) image quality assessment (IQA), task amenability potentially provides an objective measure of task-specific image quality. To predict task amenability,...
Article
Full-text available
Ultrasound is the primary modality for obstetric imaging and is highly sonographer dependent. Long training period, insufficient recruitment and poor retention of sonographers are among the global challenges in the expansion of ultrasound use. For the past several decades, technical advancements in clinical obstetric ultrasound scanning have largel...
Chapter
While performing an ultrasound (US) scan, sonographers direct their gaze at regions of interest to verify that the correct plane is acquired and to interpret the acquisition frame. Predicting sonographer gaze on US videos is useful for identification of spatio-temporal patterns that are important for US scanning. This paper investigates utilizing s...
Chapter
We propose a multiscale spatio-temporal graph convolutional network (MST-GCN) approach to learn the left ventricular (LV) motion patterns from cardiac MR image sequences. The MST-GCN follows an encoder-decoder framework. The encoder uses a sequence of multiscale graph computation units (MGCUs). The myocardial geometry is represented as a graph. The...
Conference Paper
Automated ultrasound (US)-probe movement guidance is desirable to assist inexperienced human operators during obstetric US scanning. In this paper, we present a new visual-assisted probe movement technique using automated landmark retrieval for assistive obstetric US scanning. In a first step, a set of landmarks is constructed uniformly around a...
Chapter
Deep learning models with large learning capacities often overfit to medical imaging datasets. This is because training sets are often relatively small due to the significant time and financial costs incurred in medical data acquisition and labelling. Data augmentation is therefore routinely used to expand the availability of training data and to i...
Article
Introduction Pupillometry, the measurement of eye pupil diameter, is a well-established and objective modality correlated with cognitive workload. In this paper, we analyse the pupillary response of ultrasound imaging operators to assess their cognitive workload, captured while they undertake routine fetal ultrasound examinations. Our experiments a...
Article
Full-text available
Background Gestational hypertensive and acute hypotensive disorders are associated with maternal morbidity and mortality worldwide. However, physiological blood pressure changes in pregnancy are insufficiently defined. We describe blood pressure changes across healthy pregnancies from the International Fetal and Newborn Growth Consortium for the 21...
Conference Paper
We propose a curriculum learning captioning method to caption fetal ultrasound images by training a model to dynamically transition between two different modalities (image and text) as training progresses. Specifically, we propose a course-focused dual curriculum method, where a course is training with a curriculum based on only one of the two moda...
Conference Paper
This paper presents a novel multi-modal learning approach for automated skill characterization of obstetric ultrasound operators using heterogeneous spatio-temporal sensory cues, namely, scan video, eye-tracking data, and pupillometric data, acquired in the clinical environment. We address pertinent challenges such as combining heterogeneous, small...
Preprint
Full-text available
Deep learning models with large learning capacities often overfit to medical imaging datasets. This is because training sets are often relatively small due to the significant time and financial costs incurred in medical data acquisition and labelling. Data augmentation is therefore often used to expand the availability of training data and to incre...
Chapter
We present a novel spatio-temporal graph convolutional networks (ST-GCN) approach to learn spatio-temporal patterns of left ventricular (LV) motion in cardiac MR cine images for improving the characterization of heart conditions. Specifically, a novel GCN architecture is used, where the sample nodes of endocardial and epicardial contours are connec...
Article
Full-text available
Ultrasound is a widely used imaging modality, yet it is well-known that scanning can be highly operator-dependent and difficult to perform, which limits its wider use in clinical practice. The literature on understanding what makes clinical sonography hard to learn and how sonography varies in the field is sparse, restricted to small-scale studies...
Book
This book constitutes the refereed proceedings of the 25th Conference on Medical Image Understanding and Analysis, MIUA 2021, held in July 2021. Due to COVID-19 pandemic the conference was held virtually. The 32 full papers and 8 short papers presented were carefully reviewed and selected from 77 submissions. They were organized according to follo...
Book
This book constitutes the proceedings of the Second International Workshop on Advances in Simplifying Medical UltraSound, ASMUS 2021, held on September 27, 2021, in conjunction with MICCAI 2021, the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention. The conference was planned to take place in Strasbourg, Fr...
Chapter
Visual saliency modeling for images and videos is treated as two independent tasks in recent computer vision literature. While image saliency modeling is a well-studied problem and progress on benchmarks like SALICON and MIT300 is slowing, video saliency models have shown rapid gains on the recent DHF1K benchmark. Here, we take a step back and ask:...
Chapter
We present an original automated framework for estimating gestational age (GA) from fetal ultrasound head biometry plane images. A novelty of our approach is the use of a Bayesian Neural Network (BNN), which quantifies uncertainty of the estimated GA. Knowledge of estimated uncertainty is useful in clinical decision-making, and is especially import...
Chapter
Domain adaptation is an active area of current medical image analysis research. In this paper, we present a cross-device and cross-anatomy adaptation network (CCAN) for automatically annotating fetal anomaly ultrasound video. In our approach, deep learning models trained on more widely available expert-acquired and manually-labeled free-hand ultras...
Chapter
Full-text available
For many emerging medical image analysis problems, there is limited data and associated annotations. Traditional deep learning is not well-designed for this scenario. In addition, for deploying deep models on a consumer-grade tablet, it requires models to be efficient computationally. In this paper, we describe a framework for automatic quality ass...
Preprint
Full-text available
Recently, there is an increasing demand for automatically detecting anatomical landmarks which provide rich structural information to facilitate subsequent medical image analysis. Current methods related to this task often leverage the power of deep neural networks, while a major challenge in fine tuning such models in medical applications arises f...
Article
Purpose: We present an original method for simulating realistic fetal neurosonography images specifically generating third-trimester pregnancy ultrasound images from second-trimester images. Our method was developed using unpaired data, as pairwise data were not available. We also report original insights on the general appearance differences betwe...
Chapter
Modern machine learning systems, such as convolutional neural networks rely on a rich collection of training data to learn discriminative representations. In many medical imaging applications, unfortunately, collecting a large set of well-annotated data is prohibitively expensive. To overcome data shortage and facilitate representation learning, we...
Chapter
For many medical applications, large quantities of imaging data are routinely obtained but it can be difficult and time-consuming to obtain high-quality labels for that data. We propose a novel uncertainty-based method to improve the performance of segmentation networks when limited manual labels are available in a large dataset. We estimate segmen...
Chapter
Morphological analysis of longitudinal MR images plays a key role in monitoring disease progression for prostate cancer patients, who are placed under an active surveillance program. In this paper, we describe a learning-based image registration algorithm to quantify changes on regions of interest between a pair of images from the same patient, acq...
Chapter
We present the first system that provides real-time probe movement guidance for acquiring standard planes in routine freehand obstetric ultrasound scanning. Such a system can contribute to the worldwide deployment of obstetric ultrasound scanning by lowering the required level of operator expertise. The system employs an artificial neural network t...
Chapter
In medical imaging, manual annotations can be expensive to acquire and sometimes infeasible to access, making conventional deep learning-based models difficult to scale. As a result, it would be beneficial if useful representations could be derived from raw data without the need for manual annotations. In this paper, we propose to address the probl...
Chapter
We present a novel curriculum learning approach to train a natural language processing (NLP) based fetal ultrasound image captioning model. Datasets containing medical images and corresponding textual descriptions are relatively rare and hence, smaller-sized when compared to the datasets of natural images and their captions. This fact inspired us t...
Chapter
In this paper, we consider differentiating operator skill during fetal ultrasound scanning using probe motion tracking. We present a novel convolutional neural network-based deep learning framework to model ultrasound probe motion in order to classify operator skill levels, that is invariant to operators’ personal scanning styles. In this study, pr...
Preprint
Morphological analysis of longitudinal MR images plays a key role in monitoring disease progression for prostate cancer patients, who are placed under an active surveillance program. In this paper, we describe a learning-based image registration algorithm to quantify changes on regions of interest between a pair of images from the same patient, acq...
Article
Fetal brain magnetic resonance imaging (MRI) offers exquisite images of the developing brain but is not suitable for second-trimester anomaly screening, for which ultrasound (US) is employed. Although expert sonographers are adept at reading US images, MR images which closely resemble anatomical images are much easier for non-experts to interpret....