48 citations · 113 across the 14 of their papers we have counts for
9 papers · 1 filter
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…
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 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…
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…
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…
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…
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…