Project MIRA

Next Generation Machine Intelligence for Medical Image Representation and Analysis

Publications

Algorithmic encoding of protected characteristics in chest X-ray disease detection models

Glocker et al. (2023) eBioMedicine

Measuring axiomatic soundness of counterfactual image models

Monteiro et al. (2023) ICLR

Context Label Learning: Improving Background Class Representations in Semantic Segmentation

Li et al. (2023) IEEE Transactions on Medical Imaging

Structured Uncertainty in the Observation Space of Variational Autoencoders

Langley et al. (2022) Transactions on Machine Learning Research

Federated learning enables big data for rare cancer boundary detection

Pati et al. (2022) Nature Communications

Estimating Model Performance under Domain Shifts with Class-Specific Confidence Scores

Li et al. (2022) MICCAI

Failure Detection in Medical Image Classification: A Reality Check and Benchmarking Testbed

Bernhardt et al. (2022) Transactions on Machine Learning Research

Deep Structural Causal Shape Models

Rajat et al. (2022) ECCV Causality in Vision Workshop

Frequency Dropout: Feature-Level Regularization via Randomized Filtering

Islam and Glocker (2022) ECCV Medical Computer Vision Workshop

Potential sources of dataset bias complicate investigation of underdiagnosis by machine learning algorithms

Bernhardt, Jones, Glocker (2022) Nature Medicine

Distributional Gaussian Processes Layers for Out-of-Distribution Detection

Popescu et al. (2022) Journal of Machine Learning for Biomedical Imaging

The medical algorithmic audit

Liu et al. (2022) The Lancet Digital Health

Relationship of admission blood proteomic biomarkers levels to lesion type and lesion burden in traumatic brain injury: A CENTER-TBI study

Whitehouse et al. (2022) EBioMedicie

Local Brain-Age: A U-Net Model

Popescu et al. (2021) Frontiers in Aging Neuroscience

Confidence-based Out-of-Distribution Detection: A Comparative Study and Analysis

Berger et al. (2021) MICCAI Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging

Class-Distribution-Aware Calibration for Long-Tailed Visual Recognition

Islam et al. (2021) ICML Workshop on Uncertainty and Robustness in Deep Learning

Learning from Partially Overlapping Labels: Image Segmentation under Annotation Shift

Filbrandt et al. (2021) MICCAI Workshop on Domain Adaptation and Representation Transfer

Spatially Varying Label Smoothing: Capturing Uncertainty from Expert Annotations

Islam & Glocker (2021) Information Processing in Medical Imaging

Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study

Dou et al. (2021) npj Digital Medicine

Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty

Monteiro et al. (2020) NeurIPS

Analyzing Overfitting under Class Imbalance in Neural Networks for Image Segmentation

Li, Kamnitsas, Glocker (2020) IEEE Transactions on Medical Imaging

Deep Structural Causal Models for Tractable Counterfactual Inference

Pawlowski, Castro, Glocker (2020) NeurIPS

Multi-Class Semantic Segmentation and Quantification of TBI Lesions on Head CT using Deep Learning

Monteiro & Newcombe et al. (2020) The Lancet Digital Health

Causality Matters in Medical Imaging

Castro, Walker, Glocker (2020) Nature Communications

Unpaired Multi-modal Segmentation via Knowledge Distillation

Dou et al. (2020) IEEE Transactions on Medical Imaging

Image-level Harmonization of Multi-Site Data using Image-and-Spatial Transformer Networks

Robinson et al. (2020) MICCAI

Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning

Castro et al. (2019) Journal of Machine Learning Research

TBI Lesion Segmentation in Head CT: Impact of Preprocessing and Data Augmentation

Monteiro et al. (2019) MICCAI Brain Lesion Workshop

Domain Generalization via Model-Agnostic Learning of Semantic Features

Dou et al. (2019) NeurIPS

PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network at Unpaired Cross-modality Cardiac Segmentation

Dou et al. (2019) IEEE Access

Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effects

Glocker et al. (2019) Workshop Medical Imaging meets NeurIPS

Overfitting of neural nets under class imbalance: Analysis and improvements for segmentation

Li et al. (2019) MICCAI

Is Texture Predictive for Age and Sex in Brain MRI?

Pawlowski & Glocker (2019) MIDL

Quantitative Error Prediction of Medical Image Registration using Regression Forests

Sokooti et al. (2019) Medical Image Analysis

Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels

Zlocha et al. (2019) MICCAI

Controlling Meshes via Curvature: Spin Transformations for Pose-Invariant Shape Processing

Le Folgoc et al. (2019) Information Processing in Medical Imaging

Automated Quality Control in Image Segmentation: Application to the UK Biobank Cardiovascular Magnetic Resonance Imaging Study

Robinson et al. (2019) Journal of Cardiovascular Magnetic Resonance

Semi-supervised learning via compact latent space clustering

Kamnitsas et al. (2018) ICML

Graph Convolutional Gaussian Processes

Walker & Glocker (2019) ICML

Small Organ Segmentation in Whole-body MRI using a Two-stage FCN and Weighting Schemes

Valindria et al. (2018) Workshop on Machine Learning in Medical Imaging

Towards continual learning in medical imaging

Baweja et al. (2018) Workshop Medical Imaging meets NeurIPS

Nonparametric Density Flows for MRI Intensity Normalisation

Castro & Glocker (2018) MICCAI

NeuroNet: Fast and Robust Reproduction of Multiple Brain Image Segmentation Pipelines

Rajchl et al. (2018) MIDL

Domain Adaptation for MRI Organ Segmentation using Reverse Classification Accuracy

Valindria et al. (2018) MIDL

Real-time Prediction of Segmentation Quality

Robinson et al. (2018) MICCAI

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