24 citations · 67 across the 17 of their papers we have counts for
19 papers
Finding-Aware Anatomical Tokens for Chest X-Ray Automated Reporting
Francesco Dalla Serra, Chaoyang Wang, Fani Deligianni +2
The task of radiology reporting comprises describing and interpreting the medical findings in radiographic images, including description of their location and appearance. Automated…
Compositionally Equivariant Representation Learning
Xiao Liu, Pedro Sanchez, Spyridon Thermos +2
Deep learning models often need sufficient supervision (i.e. labelled data) in order to be trained effectively. By contrast, humans can swiftly learn to identify important anatomy…
The role of noise in denoising models for anomaly detection in medical images
Antanas Kascenas, Pedro Sanchez, Patrick Schrempf +9
Pathological brain lesions exhibit diverse appearance in brain images, in terms of intensity, texture, shape, size, and location. Comprehensive sets of data and annotations are dif…
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…
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…
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…
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…
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…
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…
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…
Disentangled Representations for Domain-generalized Cardiac Segmentation
Xiao Liu, Spyridon Thermos, Agisilaos Chartsias +2
Robust cardiac image segmentation is still an open challenge due to the inability of the existing methods to achieve satisfactory performance on unseen data of different domains. S…
INSIDE: Steering Spatial Attention with Non-Imaging Information in CNNs
Grzegorz Jacenków, Alison Q. O'Neil, Brian Mohr +1
We consider the problem of integrating non-imaging information into segmentation networks to improve performance. Conditioning layers such as FiLM provide the means to selectively…
Paying Per-label Attention for Multi-label Extraction from Radiology Reports
Patrick Schrempf, Hannah Watson, Shadia Mikhael +6
Training medical image analysis models requires large amounts of expertly annotated data which is time-consuming and expensive to obtain. Images are often accompanied by free-text…
Measuring the Biases and Effectiveness of Content-Style Disentanglement
Xiao Liu, Spyridon Thermos, Gabriele Valvano +3
A recent spate of state-of-the-art semi- and un-supervised solutions disentangle and encode image "content" into a spatial tensor and image appearance or "style" into a vector, to…
Teacher-Student chain for efficient semi-supervised histology image classification
Shayne Shaw, Maciej Pajak, Aneta Lisowska +2
Deep learning shows great potential for the domain of digital pathology. An automated digital pathology system could serve as a second reader, perform initial triage in large scree…
Language Transfer for Early Warning of Epidemics from Social Media
Mattias Appelgren, Patrick Schrempf, Matúš Falis +2
Statements on social media can be analysed to identify individuals who are experiencing red flag medical symptoms, allowing early detection of the spread of disease such as influen…
Attaining human-level performance with atlas location autocontext for anatomical landmark detection in 3D CT data
Alison Q O'Neil, Antanas Kascenas, Joseph Henry +8
We present an efficient neural network method for locating anatomical landmarks in 3D medical CT scans, using atlas location autocontext in order to learn long-range spatial contex…