469 citations · 1.6k across the 93 of their papers we have counts for
16 papers · 1 filter
Clinically Plausible Pathology-Anatomy Disentanglement in Patient Brain MRI with Structured Variational Priors
Anjun Hu, Jean-Pierre R. Falet, Brennan S. Nichyporuk +4
We propose a hierarchically structured variational inference model for accurately disentangling observable evidence of disease (e.g. brain lesions or atrophy) from subject-specific…
Rethinking Generalization: The Impact of Annotation Style on Medical Image Segmentation
Brennan Nichyporuk, Jillian Cardinell, Justin Szeto +5
Generalization is an important attribute of machine learning models, particularly for those that are to be deployed in a medical context, where unreliable predictions can have real…
Diffusion Models for Causal Discovery via Topological Ordering
Pedro Sanchez, Xiao Liu, Alison Q O'Neil +1
Discovering causal relations from observational data becomes possible with additional assumptions such as considering the functional relations to be constrained as nonlinear with a…
HSIC-InfoGAN: Learning Unsupervised Disentangled Representations by Maximising Approximated Mutual Information
Xiao Liu, Spyridon Thermos, Pedro Sanchez +2
Learning disentangled representations requires either supervision or the introduction of specific model designs and learning constraints as biases. InfoGAN is a popular disentangle…
Counterfactual Image Synthesis for Discovery of Personalized Predictive Image Markers
Amar Kumar, Anjun Hu, Brennan Nichyporuk +4
The discovery of patient-specific imaging markers that are predictive of future disease outcomes can help us better understand individual-level heterogeneity of disease evolution.…
What is Healthy? Generative Counterfactual Diffusion for Lesion Localization
Pedro Sanchez, Antanas Kascenas, Xiao Liu +2
Reducing the requirement for densely annotated masks in medical image segmentation is important due to cost constraints. In this paper, we consider the problem of inferring pixel-l…
Why patient data cannot be easily forgotten?
Ruolin Su, Xiao Liu, Sotirios A. Tsaftaris
Rights provisioned within data protection regulations, permit patients to request that knowledge about their information be eliminated by data holders. With the advent of AI learne…
vMFNet: Compositionality Meets Domain-generalised Segmentation
Xiao Liu, Spyridon Thermos, Pedro Sanchez +2
Training medical image segmentation models usually requires a large amount of labeled data. By contrast, humans can quickly learn to accurately recognise anatomy of interest from m…
Metrics reloaded: Recommendations for image analysis validation
Lena Maier-Hein, Annika Reinke, Patrick Godau +71
Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen…
Causal Machine Learning for Healthcare and Precision Medicine
Pedro Sanchez, Jeremy P. Voisey, Tian Xia +3
Causal machine learning (CML) has experienced increasing popularity in healthcare. Beyond the inherent capabilities of adding domain knowledge into learning systems, CML provides a…
Unintended memorisation of unique features in neural networks
John Hartley, Sotirios A. Tsaftaris
Neural networks pose a privacy risk due to their propensity to memorise and leak training data. We show that unique features occurring only once in training data are memorised by d…
Adversarial Counterfactual Augmentation: Application in Alzheimer's Disease Classification
Tian Xia, Pedro Sanchez, Chen Qin +1
Due to the limited availability of medical data, deep learning approaches for medical image analysis tend to generalise poorly to unseen data. Augmenting data during training with…
Diffusion Causal Models for Counterfactual Estimation
Pedro Sanchez, Sotirios A. Tsaftaris
We consider the task of counterfactual estimation from observational imaging data given a known causal structure. In particular, quantifying the causal effect of interventions for…
Measuring Unintended Memorisation of Unique Private Features in Neural Networks
John Hartley, Sotirios A. Tsaftaris
Neural networks pose a privacy risk to training data due to their propensity to memorise and leak information. Focusing on image classification, we show that neural networks also u…
Indication as Prior Knowledge for Multimodal Disease Classification in Chest Radiographs with Transformers
Grzegorz Jacenków, Alison Q. O'Neil, Sotirios A. Tsaftaris
When a clinician refers a patient for an imaging exam, they include the reason (e.g. relevant patient history, suspected disease) in the scan request; this appears as the indicatio…
MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images
Lei Li, Fuping Wu, Sihan Wang +29
Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardiu…