Publications (16)
Foreground-Background Separation through Concept Distillation from Generative Image Foundation Models
Mischa Dombrowski, Hadrien Reynaud, Matthew Baugh +1
Curating datasets for object segmentation is a difficult task. With the advent of large-scale pre-trained generative models, conditional image generation has been given a significa…
Wasserstein-Aligned Localisation for VLM-Based Distributional OOD Detection in Medical Imaging
Bernhard Kainz, Johanna P Mueller, Matthew Baugh +1
Zero-shot anomaly localisation via vision-language models (VLMs) offers a compelling approach for rare pathology detection, yet its performance is fundamentally limited by the abse…
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…
Disentangling Neural Disjunctive Normal Form Models
Kexin Gu Baugh, Vincent Perreault, Matthew Baugh +3
Neural Disjunctive Normal Form (DNF) based models are powerful and interpretable approaches to neuro-symbolic learning and have shown promising results in classification and reinfo…
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…
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…