469 citations · 1.6k across the 93 of their papers we have counts for
12 papers · 1 filter
FROB: Few-shot ROBust Model for Classification and Out-of-Distribution Detection
Nikolaos Dionelis, Mehrdad Yaghoobi, Sotirios A. Tsaftaris
Nowadays, classification and Out-of-Distribution (OoD) detection in the few-shot setting remain challenging aims due to rarity and the limited samples in the few-shot setting, and…
OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the Boundary
Nikolaos Dionelis, Mehrdad Yaghoobi, Sotirios A. Tsaftaris
Generative models trained in an unsupervised manner may set high likelihood and low reconstruction loss to Out-of-Distribution (OoD) samples. This increases Type II errors and lead…
Stop Throwing Away Discriminators! Re-using Adversaries for Test-Time Training
Gabriele Valvano, Andrea Leo, Sotirios A. Tsaftaris
Thanks to their ability to learn data distributions without requiring paired data, Generative Adversarial Networks (GANs) have become an integral part of many computer vision metho…
Self-supervised Multi-scale Consistency for Weakly Supervised Segmentation Learning
Gabriele Valvano, Andrea Leo, Sotirios A. Tsaftaris
Collecting large-scale medical datasets with fine-grained annotations is time-consuming and requires experts. For this reason, weakly supervised learning aims at optimising machine…
Re-using Adversarial Mask Discriminators for Test-time Training under Distribution Shifts
Gabriele Valvano, Andrea Leo, Sotirios A. Tsaftaris
Thanks to their ability to learn flexible data-driven losses, Generative Adversarial Networks (GANs) are an integral part of many semi- and weakly-supervised methods for medical im…
Cohort Bias Adaptation in Aggregated Datasets for Lesion Segmentation
Brennan Nichyporuk, Jillian Cardinell, Justin Szeto +4
Many automatic machine learning models developed for focal pathology (e.g. lesions, tumours) detection and segmentation perform well, but do not generalize as well to new patient c…
Controllable cardiac synthesis via disentangled anatomy arithmetic
Spyridon Thermos, Xiao Liu, Alison O'Neil +1
Acquiring annotated data at scale with rare diseases or conditions remains a challenge. It would be extremely useful to have a method that controllably synthesizes images that can…
Tail of Distribution GAN (TailGAN): Generative-Adversarial-Network-Based Boundary Formation
Nikolaos Dionelis, Mehrdad Yaghoobi, Sotirios A. Tsaftaris
Generative Adversarial Networks (GAN) are a powerful methodology and can be used for unsupervised anomaly detection, where current techniques have limitations such as the accurate…
Boundary of Distribution Support Generator (BDSG): Sample Generation on the Boundary
Nikolaos Dionelis, Mehrdad Yaghoobi, Sotirios A. Tsaftaris
Generative models, such as Generative Adversarial Networks (GANs), have been used for unsupervised anomaly detection. While performance keeps improving, several limitations exist p…
Survey: Leakage and Privacy at Inference Time
Marija Jegorova, Chaitanya Kaul, Charlie Mayor +4
Leakage of data from publicly available Machine Learning (ML) models is an area of growing significance as commercial and government applications of ML can draw on multiple sources…
Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image Segmentation
Xiao Liu, Spyridon Thermos, Alison O'Neil +1
Generalising deep models to new data from new centres (termed here domains) remains a challenge. This is largely attributed to shifts in data statistics (domain shifts) between sou…
Common Limitations of Image Processing Metrics: A Picture Story
Annika Reinke, Minu D. Tizabi, Carole H. Sudre +90
While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are…