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Found 14 papers, 6 papers with code
Counterfactual contrastive learning: robust representations via causal image synthesis
Contrastive pretraining is well-known to improve downstream task performance and model generalisation, especially in limited label settings.
Mitigating attribute amplification in counterfactual image generation
Causal generative modelling is gaining interest in medical imaging due to its ability to answer interventional and counterfactual queries.
Demystifying Variational Diffusion Models
Despite the growing popularity of diffusion models, gaining a deep understanding of the model class remains somewhat elusive for the uninitiated in non-equilibrium statistical physics.
No Fair Lunch: A Causal Perspective on Dataset Bias in Machine Learning for Medical Imaging
As machine learning methods gain prominence within clinical decision-making, addressing fairness concerns becomes increasingly urgent.
Grounded Object Centric Learning
The extraction of modular object-centric representations for downstream tasks is an emerging area of research.
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.
Learning with Capsules: A Survey
The aim of this survey is to provide a comprehensive overview of the capsule network research landscape, which will serve as a valuable resource for the community going forward.
Failure Detection in Medical Image Classification: A Reality Check and Benchmarking Testbed
Failure detection in automated image classification is a critical safeguard for clinical deployment.
Introducing Routing Uncertainty in Capsule Networks
Rather than performing inefficient local iterative routing between adjacent capsule layers, we propose an alternative global view based on representing the inherent uncertainty in part-object assignment.
Capsule Routing via Variational Bayes
Capsule networks are a recently proposed type of neural network shown to outperform alternatives in challenging shape recognition tasks.
Ranked #3 on
Image Classification
on smallNORB
Deep Bayesian Self-Training
Supervised Deep Learning has been highly successful in recent years, achieving state-of-the-art results in most tasks.
Towards a Deep Unified Framework for Nuclear Reactor Perturbation Analysis
512 dimensional representations were extracted from the 3D-CNN and LSTM architectures, and used as input to a fused multi-sigmoid classification layer to recognise the perturbation type.
