activity
20182023
most citedNatural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization

48 citations · 113 across the 14 of their papers we have counts for

collaborators
Showing 2020Show all

5 papers · 1 filter

eess.IV2020

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…

cs.CV2020★ 40 cited

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…

eess.IV2020

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…

cs.CV2020★ 4 cited

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…

cs.NE2020

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…