no code implementations • 29 May 2024 • Raman Dutt, Pedro Sanchez, Ondrej Bohdal, Sotirios A. Tsaftaris, Timothy Hospedales
We perform our experiments for the specific task of medical image generation and outperform existing state-of-the-art training-time mitigation strategies by fine-tuning as few as 0. 019% of model parameters.
no code implementations • 19 Apr 2024 • Konstantinos Vilouras, Pedro Sanchez, Alison Q. O'Neil, Sotirios A. Tsaftaris
In this work, we use a publicly available Foundation Model, namely the Latent Diffusion Model, to solve this challenging task.
1 code implementation • 29 Mar 2024 • Thomas Melistas, Nikos Spyrou, Nefeli Gkouti, Pedro Sanchez, Athanasios Vlontzos, Giorgos Papanastasiou, Sotirios A. Tsaftaris
Counterfactual image generation is pivotal for understanding the causal relations of variables, with applications in interpretability and generation of unbiased synthetic data.
no code implementations • 1 Nov 2023 • Konstantinos Vilouras, Xiao Liu, Pedro Sanchez, Alison Q. O'Neil, Sotirios A. Tsaftaris
Knowledge distillation enables fast and effective transfer of features learned from a bigger model to a smaller one.
2 code implementations • 27 Jul 2023 • Walter H. L. Pinaya, Mark S. Graham, Eric Kerfoot, Petru-Daniel Tudosiu, Jessica Dafflon, Virginia Fernandez, Pedro Sanchez, Julia Wolleb, Pedro F. da Costa, Ashay Patel, Hyungjin Chung, Can Zhao, Wei Peng, Zelong Liu, Xueyan Mei, Oeslle Lucena, Jong Chul Ye, Sotirios A. Tsaftaris, Prerna Dogra, Andrew Feng, Marc Modat, Parashkev Nachev, Sebastien Ourselin, M. Jorge Cardoso
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.
no code implementations • 11 Jul 2023 • Avinash Kori, Pedro Sanchez, Konstantinos Vilouras, Ben Glocker, Sotirios A. Tsaftaris
Unsupervised representation learning with variational inference relies heavily on independence assumptions over latent variables.
no code implementations • 13 Jun 2023 • Xiao Liu, Pedro Sanchez, Spyridon Thermos, Alison Q. O'Neil, Sotirios A. Tsaftaris
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.
no code implementations • 2 Jun 2023 • Virginia Fernandez, Pedro Sanchez, Walter Hugo Lopez Pinaya, Grzegorz Jacenków, Sotirios A. Tsaftaris, Jorge Cardoso
Knowledge distillation in neural networks refers to compressing a large model or dataset into a smaller version of itself.
no code implementations • 14 May 2023 • Raman Dutt, Linus Ericsson, Pedro Sanchez, Sotirios A. Tsaftaris, Timothy Hospedales
We present a comprehensive evaluation of Parameter-Efficient Fine-Tuning (PEFT) techniques for diverse medical image analysis tasks.
1 code implementation • 19 Jan 2023 • Antanas Kascenas, Pedro Sanchez, Patrick Schrempf, Chaoyang Wang, William Clackett, Shadia S. Mikhael, Jeremy P. Voisey, Keith Goatman, Alexander Weir, Nicolas Pugeault, Sotirios A. Tsaftaris, Alison Q. O'Neil
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.
1 code implementation • 12 Oct 2022 • Pedro Sanchez, Xiao Liu, Alison Q O'Neil, Sotirios A. Tsaftaris
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.
1 code implementation • 6 Aug 2022 • Xiao Liu, Spyridon Thermos, Pedro Sanchez, Alison Q. O'Neil, Sotirios A. Tsaftaris
Maximisation of mutual information is achieved by introducing an auxiliary network and training with a latent regression loss.
1 code implementation • 25 Jul 2022 • Pedro Sanchez, Antanas Kascenas, Xiao Liu, Alison Q. O'Neil, Sotirios A. Tsaftaris
This requires training with healthy and unhealthy data in DPMs.
1 code implementation • 29 Jun 2022 • Xiao Liu, Spyridon Thermos, Pedro Sanchez, Alison Q. O'Neil, Sotirios A. Tsaftaris
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.
no code implementations • 23 May 2022 • Pedro Sanchez, Jeremy P. Voisey, Tian Xia, Hannah I. Watson, Alison Q. ONeil, Sotirios A. Tsaftaris
Causal machine learning (CML) has experienced increasing popularity in healthcare.
no code implementations • 15 Mar 2022 • Tian Xia, Pedro Sanchez, Chen Qin, Sotirios A. Tsaftaris
To demonstrate the effectiveness of the proposed approach, we validate the method with the classification of Alzheimer's Disease (AD) as a downstream task.
1 code implementation • 21 Feb 2022 • Pedro Sanchez, Sotirios A. Tsaftaris
We consider the task of counterfactual estimation from observational imaging data given a known causal structure.
1 code implementation • 26 Aug 2021 • Xiao Liu, Pedro Sanchez, Spyridon Thermos, Alison Q. O'Neil, Sotirios A. Tsaftaris
Disentangled representation learning has been proposed as an approach to learning general representations even in the absence of, or with limited, supervision.