Search Results for author: Pedro Sanchez

Found 18 papers, 9 papers with code

MemControl: Mitigating Memorization in Medical Diffusion Models via Automated Parameter Selection

no code implementations29 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.

Image Generation Medical Image Generation +1

Zero-Shot Medical Phrase Grounding with Off-the-shelf Diffusion Models

no code implementations19 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.

Contrastive Learning Phrase Grounding

Group Distributionally Robust Knowledge Distillation

no code implementations1 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.

Knowledge Distillation

Generative AI for Medical Imaging: extending the MONAI Framework

2 code implementations27 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.

Anomaly Detection Denoising +2

A Causal Ordering Prior for Unsupervised Representation Learning

no code implementations11 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.

Causal Discovery counterfactual +2

Compositionally Equivariant Representation Learning

no code implementations13 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.

Anatomy Image Segmentation +3

The role of noise in denoising models for anomaly detection in medical images

1 code implementation19 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.

Denoising Unsupervised Anomaly Detection

Diffusion Models for Causal Discovery via Topological Ordering

1 code implementation12 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.

Causal Discovery

vMFNet: Compositionality Meets Domain-generalised Segmentation

1 code implementation29 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.

Anatomy Image Segmentation +3

Adversarial Counterfactual Augmentation: Application in Alzheimer's Disease Classification

no code implementations15 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.

Classification counterfactual +1

Diffusion Causal Models for Counterfactual Estimation

1 code implementation21 Feb 2022 Pedro Sanchez, Sotirios A. Tsaftaris

We consider the task of counterfactual estimation from observational imaging data given a known causal structure.


Learning Disentangled Representations in the Imaging Domain

1 code implementation26 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.

Representation Learning

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