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Found 8 papers, 6 papers with code
High Fidelity Image Counterfactuals with Probabilistic Causal Models
We present a general causal generative modelling framework for accurate estimation of high fidelity image counterfactuals with deep structural causal models.
Measuring axiomatic soundness of counterfactual image models
We present a general framework for evaluating image counterfactuals.
Analysing the effectiveness of a generative model for semi-supervised medical image segmentation
In such settings, semi-supervised learning (SSL) attempts to leverage the abundance of unlabelled data to obtain more robust and reliable models.
Automatic lesion analysis for increased efficiency in outcome prediction of traumatic brain injury
We compare the predictive power of our proposed features to the Marshall score, independently and when paired with classic TBI biomarkers.
Structured Uncertainty in the Observation Space of Variational Autoencoders
In contrast, improving the model for the observational distribution is rarely considered and typically defaults to a pixel-wise independent categorical or normal distribution.
Active label cleaning for improved dataset quality under resource constraints
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 of model performance.
Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty
In image segmentation, there is often more than one plausible solution for a given input.
Conditional Random Fields as Recurrent Neural Networks for 3D Medical Imaging Segmentation
In this paper, we test whether this algorithm, which was shown to improve semantic segmentation for 2D RGB images, is able to improve segmentation quality for 3D multi-modal medical images.
