48 citations · 113 across the 14 of their papers we have counts for
16 papers
Many tasks make light work: Learning to localise medical anomalies from multiple synthetic tasks
Matthew Baugh, Jeremy Tan, Johanna P. Müller +3
There is a growing interest in single-class modelling and out-of-distribution detection as fully supervised machine learning models cannot reliably identify classes not included in…
Confidence-Aware and Self-Supervised Image Anomaly Localisation
Johanna P. Müller, Matthew Baugh, Jeremy Tan +2
Universal anomaly detection still remains a challenging problem in machine learning and medical image analysis. It is possible to learn an expected distribution from a single class…
Adnexal Mass Segmentation with Ultrasound Data Synthesis
Clara Lebbos, Jen Barcroft, Jeremy Tan +5
Ovarian cancer is the most lethal gynaecological malignancy. The disease is most commonly asymptomatic at its early stages and its diagnosis relies on expert evaluation of transvag…
nnOOD: A Framework for Benchmarking Self-supervised Anomaly Localisation Methods
Matthew Baugh, Jeremy Tan, Athanasios Vlontzos +2
The wide variety of in-distribution and out-of-distribution data in medical imaging makes universal anomaly detection a challenging task. Recently a number of self-supervised metho…
Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization
Hannah M. Schlüter, Jeremy Tan, Benjamin Hou +1
We introduce a simple and intuitive self-supervision task, Natural Synthetic Anomalies (NSA), for training an end-to-end model for anomaly detection and localization using only nor…
Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-specific Atlas Maps
Samuel Budd, Matthew Sinclair, Thomas Day +11
Fetal ultrasound screening during pregnancy plays a vital role in the early detection of fetal malformations which have potential long-term health impacts. The level of skill requi…
Detecting Outliers with Poisson Image Interpolation
Jeremy Tan, Benjamin Hou, Thomas Day +3
Supervised learning of every possible pathology is unrealistic for many primary care applications like health screening. Image anomaly detection methods that learn normal appearanc…
Learning normal appearance for fetal anomaly screening: Application to the unsupervised detection of Hypoplastic Left Heart Syndrome
Elisa Chotzoglou, Thomas Day, Jeremy Tan +5
Congenital heart disease is considered as one the most common groups of congenital malformations which affects per newborns. In this work, an automated framework for…
Detecting Outliers with Foreign Patch Interpolation
Jeremy Tan, Benjamin Hou, James Batten +2
In medical imaging, outliers can contain hypo/hyper-intensities, minor deformations, or completely altered anatomy. To detect these irregularities it is helpful to learn the featur…
Automated Detection of Congenital Heart Disease in Fetal Ultrasound Screening
Jeremy Tan, Anselm Au, Qingjie Meng +7
Prenatal screening with ultrasound can lower neonatal mortality significantly for selected cardiac abnormalities. However, the need for human expertise, coupled with the high volum…
Ultrasound Video Summarization using Deep Reinforcement Learning
Tianrui Liu, Qingjie Meng, Athanasios Vlontzos +3
Video is an essential imaging modality for diagnostics, e.g. in ultrasound imaging, for endoscopy, or movement assessment. However, video hasn't received a lot of attention in the…
Divergent Search for Few-Shot Image Classification
Jeremy Tan, Bernhard Kainz
When data is unlabelled and the target task is not known a priori, divergent search offers a strategy for learning a wide range of skills. Having such a repertoire allows a system…
Semi-supervised Learning of Fetal Anatomy from Ultrasound
Jeremy Tan, Anselm Au, Qingjie Meng +1
Semi-supervised learning methods have achieved excellent performance on standard benchmark datasets using very few labelled images. Anatomy classification in fetal 2D ultrasound is…
Controlling Meshes via Curvature: Spin Transformations for Pose-Invariant Shape Processing
Loic Le Folgoc, Daniel C. Castro, Jeremy Tan +5
We investigate discrete spin transformations, a geometric framework to manipulate surface meshes by controlling mean curvature. Applications include surface fairing -- flowing a me…
Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning
Daniel C. Castro, Jeremy Tan, Bernhard Kainz +2
Revealing latent structure in data is an active field of research, having introduced exciting technologies such as variational autoencoders and adversarial networks, and is essenti…
Deep Adversarial Context-Aware Landmark Detection for Ultrasound Imaging
Ahmet Tuysuzoglu, Jeremy Tan, Kareem Eissa +3
Real-time localization of prostate gland in trans-rectal ultrasound images is a key technology that is required to automate the ultrasound guided prostate biopsy procedures. In thi…