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Found 16 papers, 9 papers with code
Benchmarking Counterfactual Image Generation
Counterfactual image generation is pivotal for understanding the causal relations of variables, with applications in interpretability and generation of unbiased synthetic data.
Group Distributionally Robust Knowledge Distillation
Knowledge distillation enables fast and effective transfer of features learned from a bigger model to a smaller one.
Generative AI for Medical Imaging: extending the MONAI Framework
We have implemented these models in a generalisable fashion, illustrating that their results can be extended to 2D or 3D scenarios, including medical images with different modalities (like CT, MRI, and X-Ray data) and from different anatomical areas.
A Causal Ordering Prior for Unsupervised Representation Learning
Unsupervised representation learning with variational inference relies heavily on independence assumptions over latent variables.
Compositionally Equivariant Representation Learning
By modelling the compositional representations with learnable von-Mises-Fisher (vMF) kernels, we explore how different design and learning biases can be used to enforce the representations to be more compositionally equivariant under un-, weakly-, and semi-supervised settings.
Privacy Distillation: Reducing Re-identification Risk of Multimodal Diffusion Models
Knowledge distillation in neural networks refers to compressing a large model or dataset into a smaller version of itself.
Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity
We present a comprehensive evaluation of Parameter-Efficient Fine-Tuning (PEFT) techniques for diverse medical image analysis tasks.
The role of noise in denoising models for anomaly detection in medical images
Denoising methods, for instance classical denoising autoencoders (DAEs) and more recently emerging diffusion models, are a promising approach, however naive application of pixelwise noise leads to poor anomaly detection performance.
Diffusion Models for Causal Discovery via Topological Ordering
We introduce theory for updating the learned Hessian without re-training the neural network, and we show that computing with a subset of samples gives an accurate approximation of the ordering, which allows scaling to datasets with more samples and variables.
HSIC-InfoGAN: Learning Unsupervised Disentangled Representations by Maximising Approximated Mutual Information
Maximisation of mutual information is achieved by introducing an auxiliary network and training with a latent regression loss.
What is Healthy? Generative Counterfactual Diffusion for Lesion Localization
This requires training with healthy and unhealthy data in DPMs.
vMFNet: Compositionality Meets Domain-generalised Segmentation
Moreover, with a reconstruction module, unlabeled data can also be used to learn the vMF kernels and likelihoods by recombining them to reconstruct the input image.
Causal Machine Learning for Healthcare and Precision Medicine
Causal machine learning (CML) has experienced increasing popularity in healthcare.
Adversarial Counterfactual Augmentation: Application in Alzheimer's Disease Classification
To demonstrate the effectiveness of the proposed approach, we validate the method with the classification of Alzheimer's Disease (AD) as a downstream task.
Diffusion Causal Models for Counterfactual Estimation
We consider the task of counterfactual estimation from observational imaging data given a known causal structure.
Learning Disentangled Representations in the Imaging Domain
Disentangled representation learning has been proposed as an approach to learning general representations even in the absence of, or with limited, supervision.
