430 citations · 1.5k across the 20 of their papers we have counts for
20 papers
No Fair Lunch: A Causal Perspective on Dataset Bias in Machine Learning for Medical Imaging
Charles Jones, Daniel C. Castro, Fabio De Sousa Ribeiro +3
As machine learning methods gain prominence within clinical decision-making, addressing fairness concerns becomes increasingly urgent. Despite considerable work dedicated to detect…
Measuring axiomatic soundness of counterfactual image models
Miguel Monteiro, Fabio De Sousa Ribeiro, Nick Pawlowski +2
We present a general framework for evaluating image counterfactuals. The power and flexibility of deep generative models make them valuable tools for learning mechanisms in structu…
Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing
Shruthi Bannur, Stephanie Hyland, Qianchu Liu +13
Self-supervised learning in vision-language processing exploits semantic alignment between imaging and text modalities. Prior work in biomedical VLP has mostly relied on the alignm…
Analysing the effectiveness of a generative model for semi-supervised medical image segmentation
Margherita Rosnati, Fabio De Sousa Ribeiro, Miguel Monteiro +2
Image segmentation is important in medical imaging, providing valuable, quantitative information for clinical decision-making in diagnosis, therapy, and intervention. The state-of-…
Deep Structural Causal Shape Models
Rajat Rasal, Daniel C. Castro, Nick Pawlowski +1
Causal reasoning provides a language to ask important interventional and counterfactual questions beyond purely statistical association. In medical imaging, for example, we may wan…
Making the Most of Text Semantics to Improve Biomedical Vision--Language Processing
Benedikt Boecking, Naoto Usuyama, Shruthi Bannur +9
Multi-modal data abounds in biomedicine, such as radiology images and reports. Interpreting this data at scale is essential for improving clinical care and accelerating clinical re…
Active label cleaning for improved dataset quality under resource constraints
Melanie Bernhardt, Daniel C. Castro, Ryutaro Tanno +9
Imperfections in data annotation, known as label noise, are detrimental to the training of machine learning models and have an often-overlooked confounding effect on the assessment…
Hierarchical Analysis of Visual COVID-19 Features from Chest Radiographs
Shruthi Bannur, Ozan Oktay, Melanie Bernhardt +9
Chest radiography has been a recommended procedure for patient triaging and resource management in intensive care units (ICUs) throughout the COVID-19 pandemic. The machine learnin…
Image-level Harmonization of Multi-Site Data using Image-and-Spatial Transformer Networks
R. Robinson, Q. Dou, D. C. Castro +5
We investigate the use of image-and-spatial transformer networks (ISTNs) to tackle domain shift in multi-site medical imaging data. Commonly, domain adaptation (DA) is performed wi…
Deep Structural Causal Models for Tractable Counterfactual Inference
Nick Pawlowski, Daniel C. Castro, Ben Glocker
We formulate a general framework for building structural causal models (SCMs) with deep learning components. The proposed approach employs normalising flows and variational inferen…
Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty
Miguel Monteiro, Loïc Le Folgoc, Daniel Coelho de Castro +5
In image segmentation, there is often more than one plausible solution for a given input. In medical imaging, for example, experts will often disagree about the exact location of o…
Causality matters in medical imaging
Daniel C. Castro, Ian Walker, Ben Glocker
This article discusses how the language of causality can shed new light on the major challenges in machine learning for medical imaging: 1) data scarcity, which is the limited avai…
Domain Generalization via Model-Agnostic Learning of Semantic Features
Qi Dou, Daniel C. Castro, Konstantinos Kamnitsas +1
Generalization capability to unseen domains is crucial for machine learning models when deploying to real-world conditions. We investigate the challenging problem of domain general…
Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects
Ben Glocker, Robert Robinson, Daniel C. Castro +2
This is an empirical study to investigate the impact of scanner effects when using machine learning on multi-site neuroimaging data. We utilize structural T1-weighted brain MRI obt…
Controlling Meshes via Curvature: Spin Transformations for Pose-Invariant Shape Processing
Loic Le Folgoc, Daniel C. Castro, Jeremy Tan +5
We investigate discrete spin transformations, a geometric framework to manipulate surface meshes by controlling mean curvature. Applications include surface fairing -- flowing a me…
Contextual Face Recognition with a Nested-Hierarchical Nonparametric Identity Model
Daniel C. Castro, Sebastian Nowozin
Current face recognition systems typically operate via classification into known identities obtained from supervised identity annotations. There are two problems with this paradigm…
Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning
Daniel C. Castro, Jeremy Tan, Bernhard Kainz +2
Revealing latent structure in data is an active field of research, having introduced exciting technologies such as variational autoencoders and adversarial networks, and is essenti…
Semi-Supervised Learning via Compact Latent Space Clustering
Konstantinos Kamnitsas, Daniel C. Castro, Loic Le Folgoc +6
We present a novel cost function for semi-supervised learning of neural networks that encourages compact clustering of the latent space to facilitate separation. The key idea is to…
From Face Recognition to Models of Identity: A Bayesian Approach to Learning about Unknown Identities from Unsupervised Data
Daniel C. Castro, Sebastian Nowozin
Current face recognition systems robustly recognize identities across a wide variety of imaging conditions. In these systems recognition is performed via classification into known…
Nonparametric Density Flows for MRI Intensity Normalisation
Daniel C. Castro, Ben Glocker
With the adoption of powerful machine learning methods in medical image analysis, it is becoming increasingly desirable to aggregate data that is acquired across multiple sites. Ho…