Search Results for author: Esther Puyol-Anton

Found 13 papers, 0 papers with code

Addressing Deep Learning Model Calibration Using Evidential Neural Networks and Uncertainty-Aware Training

no code implementations30 Jan 2023 Tareen Dawood, Emily Chan, Reza Razavi, Andrew P. King, Esther Puyol-Anton

However, in our complex artefact detection task, we saw an improvement in calibration for both a low and higher-capacity model when implementing both the ENN and uncertainty-aware training together, indicating that this approach can offer a promising way to improve calibration in such settings.


A systematic study of race and sex bias in CNN-based cardiac MR segmentation

no code implementations4 Sep 2022 Tiarna Lee, Esther Puyol-Anton, Bram Ruijsink, Miaojing Shi, Andrew P. King

We present the first systematic study of the impact of training set imbalance on race and sex bias in CNN-based segmentation.


Deep Learning-based Segmentation of Pleural Effusion From Ultrasound Using Coordinate Convolutions

no code implementations5 Aug 2022 Germain Morilhat, Naomi Kifle, Sandra FinesilverSmith, Bram Ruijsink, Vittoria Vergani, Habtamu Tegegne Desita, Zerubabel Tegegne Desita, Esther Puyol-Anton, Aaron Carass, Andrew P. King

This work showcases, for the first time, the potential of DL in automating the process of effusion assessment from ultrasound in LMIC settings where there is often a lack of experienced radiologists to perform such tasks.

Quality-aware Cine Cardiac MRI Reconstruction and Analysis from Undersampled k-space Data

no code implementations16 Sep 2021 Ines Machado, Esther Puyol-Anton, Kerstin Hammernik, Gastao Cruz, Devran Ugurlu, Bram Ruijsink, Miguel Castelo-Branco, Alistair Young, Claudia Prieto, Julia A. Schnabel, Andrew P. King

The framework consists of a deep learning model for the reconstruction of 2D+t cardiac cine MRI images from undersampled data, an image quality-control step to detect good quality reconstructions, followed by a deep learning model for bi-ventricular segmentation, a quality-control step to detect good quality segmentations and automated calculation of cardiac functional parameters.

MRI Reconstruction

Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation

no code implementations23 Jun 2021 Esther Puyol-Anton, Bram Ruijsink, Stefan K. Piechnik, Stefan Neubauer, Steffen E. Petersen, Reza Razavi, Andrew P. King

The subject of "fairness" in artificial intelligence (AI) refers to assessing AI algorithms for potential bias based on demographic characteristics such as race and gender, and the development of algorithms to address this bias.

Fairness Meta-Learning

Deep Learning for Automatic Spleen Length Measurement in Sickle Cell Disease Patients

no code implementations6 Sep 2020 Zhen Yuan, Esther Puyol-Anton, Haran Jogeesvaran, Catriona Reid, Baba Inusa, Andrew P. King

To the best of our knowledge, this is the first attempt to measure spleen size in a fully automated way from ultrasound images.

Quality-aware semi-supervised learning for CMR segmentation

no code implementations1 Sep 2020 Bram Ruijsink, Esther Puyol-Anton, Ye Li, Wenja Bai, Eric Kerfoot, Reza Razavi, Andrew P. King

SemiQCSeg can be an efficient approach for training segmentation networks for medical image data when labelled datasets are scarce.

Data Augmentation Image Segmentation +2

Global and Local Interpretability for Cardiac MRI Classification

no code implementations14 Jun 2019 James R. Clough, Ilkay Oksuz, Esther Puyol-Anton, Bram Ruijsink, Andrew P. King, Julia A. Schnabel

Deep learning methods for classifying medical images have demonstrated impressive accuracy in a wide range of tasks but often these models are hard to interpret, limiting their applicability in clinical practice.

Classification General Classification +1

Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning

no code implementations29 Oct 2018 Ilkay Oksuz, Bram Ruijsink, Esther Puyol-Anton, James Clough, Gastao Cruz, Aurelien Bustin, Claudia Prieto, Rene Botnar, Daniel Rueckert, Julia A. Schnabel, Andrew P. King

Due to the high number of good quality images compared to the relatively low number of images with motion artefacts, we propose a novel data augmentation scheme based on synthetic artefact creation in k-space.

Data Augmentation General Classification +2

Deep Learning using K-space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection

no code implementations15 Aug 2018 Ilkay Oksuz, Bram Ruijsink, Esther Puyol-Anton, Aurelien Bustin, Gastao Cruz, Claudia Prieto, Daniel Rueckert, Julia A. Schnabel, Andrew P. King

As this is a highly imbalanced classification problem (due to the high number of good quality images compared to the low number of images with motion artefacts), we propose a novel k-space based training data augmentation approach in order to address this problem.

Data Augmentation Image Quality Assessment +1

Learning associations between clinical information and motion-based descriptors using a large scale MR-derived cardiac motion atlas

no code implementations27 Jul 2018 Esther Puyol-Anton, Bram Ruijsink, Helene Langet, Mathieu De Craene, Paolo Piro, Julia A. Schnabel, Andrew P. King

The availability of large scale databases containing imaging and non-imaging data, such as the UK Biobank, represents an opportunity to improve our understanding of healthy and diseased bodily function.

Dimensionality Reduction

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