Search Results for author: Daniel C. Alexander

Found 50 papers, 29 papers with code

Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain Imaging

1 code implementation28 Nov 2023 Peirong Liu, Oula Puonti, Xiaoling Hu, Daniel C. Alexander, Juan E. Iglesias

We present new metrics to validate the intra- and inter-subject robustness of Brain-ID features, and evaluate their performance on four downstream applications, covering contrast-independent (anatomy reconstruction/contrast synthesis, brain segmentation), and contrast-dependent (super-resolution, bias field estimation) tasks.

Anatomy Brain Segmentation +2

A 3D Conditional Diffusion Model for Image Quality Transfer -- An Application to Low-Field MRI

1 code implementation11 Nov 2023 Seunghoi Kim, Henry F. J. Tregidgo, Ahmed K. Eldaly, Matteo Figini, Daniel C. Alexander

Low-field (LF) MRI scanners (<1T) are still prevalent in settings with limited resources or unreliable power supply.

CenTime: Event-Conditional Modelling of Censoring in Survival Analysis

2 code implementations7 Sep 2023 Ahmed H. Shahin, An Zhao, Alexander C. Whitehead, Daniel C. Alexander, Joseph Jacob, David Barber

We demonstrate that our approach forms a consistent estimator for the event model parameters, even in the absence of uncensored data.

Survival Analysis

A coupled-mechanisms modelling framework for neurodegeneration

no code implementations10 Aug 2023 Tiantian He, Elinor Thompson, Anna Schroder, Neil P. Oxtoby, Ahmed Abdulaal, Frederik Barkhof, Daniel C. Alexander

We account for the heterogeneity of disease by fitting the model at the individual level, allowing the epicenters and rate of progression to vary among subjects.

Feature Importance Model Selection

Rician likelihood loss for quantitative MRI using self-supervised deep learning

no code implementations13 Jul 2023 Christopher S. Parker, Anna Schroder, Sean C. Epstein, James Cole, Daniel C. Alexander, HUI ZHANG

Results: Networks trained with NLR loss show higher estimation accuracy than MSE for the ADC and IVIM diffusion coefficients as SNR decreases, with minimal loss of precision or total error.

Domain-agnostic segmentation of thalamic nuclei from joint structural and diffusion MRI

no code implementations5 May 2023 Henry F. J. Tregidgo, Sonja Soskic, Mark D. Olchanyi, Juri Althonayan, Benjamin Billot, Chiara Maffei, Polina Golland, Anastasia Yendiki, Daniel C. Alexander, Martina Bocchetta, Jonathan D. Rohrer, Juan Eugenio Iglesias

Some tools have attempted to incorporate information from diffusion MRI in the segmentation to refine these boundaries, but do not generalise well across diffusion MRI acquisitions.

Segmentation

Expectation Maximization Pseudo Labels

1 code implementation2 May 2023 MouCheng Xu, Yukun Zhou, Chen Jin, Marius de Groot, Daniel C. Alexander, Neil P. Oxtoby, Yipeng Hu, Joseph Jacob

In the remainder of the paper, we showcase the applications of pseudo-labelling and its generalised form, Bayesian Pseudo-Labelling, in the semi-supervised segmentation of medical images.

Segmentation

Low-field magnetic resonance image enhancement via stochastic image quality transfer

1 code implementation26 Apr 2023 Hongxiang Lin, Matteo Figini, Felice D'Arco, Godwin Ogbole, Ryutaro Tanno, Stefano B. Blumberg, Lisa Ronan, Biobele J. Brown, David W. Carmichael, Ikeoluwa Lagunju, Judith Helen Cross, Delmiro Fernandez-Reyes, Daniel C. Alexander

Here, we present Image Quality Transfer (IQT) to enhance low-field structural MRI by estimating from a low-field image the image we would have obtained from the same subject at high field.

Image Enhancement

Deformably-Scaled Transposed Convolution

no code implementations17 Oct 2022 Stefano B. Blumberg, Daniele Raví, Mou-Cheng Xu, Matteo Figini, Iasonas Kokkinos, Daniel C. Alexander

Transposed convolution is crucial for generating high-resolution outputs, yet has received little attention compared to convolution layers.

Image Enhancement Instance Segmentation +3

Experimental Design for Multi-Channel Imaging via Task-Driven Feature Selection

1 code implementation13 Oct 2022 Stefano B. Blumberg, Paddy J. Slator, Daniel C. Alexander

Here we present a new paradigm for experimental design that simultaneously optimizes the design (set of image channels) and trains a machine-learning model to execute a user-specified image-analysis task.

Experimental Design feature selection

An efficient semi-supervised quality control system trained using physics-based MRI-artefact generators and adversarial training

no code implementations7 Jun 2022 Daniele Ravi, Frederik Barkhof, Daniel C. Alexander, Lemuel Puglisi, Geoffrey JM Parker, Arman Eshaghi

To tackle this problem, we propose a framework with four main components: 1) artefact generators inspired by magnetic resonance physics to corrupt brain MRI scans and augment a training dataset, 2) abstract and engineered features to represent images compactly, 3) a feature selection process depending on the artefact class to improve classification, and 4) SVM classifiers to identify artefacts.

Computational Efficiency Data Augmentation +1

Survival Analysis for Idiopathic Pulmonary Fibrosis using CT Images and Incomplete Clinical Data

2 code implementations21 Mar 2022 Ahmed H. Shahin, Joseph Jacob, Daniel C. Alexander, David Barber

To this end, we propose a probabilistic model that captures the dependencies between the observed clinical variables and imputes missing ones.

Imputation Survival Analysis

Learning Morphological Feature Perturbations for Calibrated Semi-Supervised Segmentation

1 code implementation19 Mar 2022 Mou-Cheng Xu, Yu-Kun Zhou, Chen Jin, Stefano B Blumberg, Frederick J Wilson, Marius deGroot, Daniel C. Alexander, Neil P. Oxtoby, Joseph Jacob

We propose MisMatch, a novel consistency-driven semi-supervised segmentation framework which produces predictions that are invariant to learnt feature perturbations.

Segmentation

Progressive Subsampling for Oversampled Data -- Application to Quantitative MRI

1 code implementation17 Mar 2022 Stefano B. Blumberg, Hongxiang Lin, Francesco Grussu, Yukun Zhou, Matteo Figini, Daniel C. Alexander

We build upon a recent dual-network approach that won the MICCAI MUlti-DIffusion (MUDI) quantitative MRI measurement sampling-reconstruction challenge, but suffers from deep learning training instability, by subsampling with a hard decision boundary.

Neural Architecture Search

VAFO-Loss: VAscular Feature Optimised Loss Function for Retinal Artery/Vein Segmentation

no code implementations12 Mar 2022 Yukun Zhou, MouCheng Xu, Yipeng Hu, Stefano B. Blumberg, An Zhao, Siegfried K. Wagner, Pearse A. Keane, Daniel C. Alexander

Estimating clinically-relevant vascular features following vessel segmentation is a standard pipeline for retinal vessel analysis, which provides potential ocular biomarkers for both ophthalmic disease and systemic disease.

Segmentation

Ten years of image analysis and machine learning competitions in dementia

no code implementations15 Dec 2021 Esther E. Bron, Stefan Klein, Annika Reinke, Janne M. Papma, Lena Maier-Hein, Daniel C. Alexander, Neil P. Oxtoby

Key for increasing impact in this way are larger testing data sizes, which could be reached by sharing algorithms rather than data to exploit data that cannot be shared.

BIG-bench Machine Learning

AGCN: Adversarial Graph Convolutional Network for 3D Point Cloud Segmentation

2 code implementations British Machine Vision Conference (BMVC) 2021 Seunghoi Kim, Daniel C. Alexander

To overcome these problems, we propose a) a graph convolutional network (GCN) in an adversarial learning scheme where a discriminator network provides a segmentation network with informative information to improve segmentation accuracy and b) a graph convolution, GeoEdgeConv, as a means of local feature aggregation to improve segmentation accuracy and space and time complexities.

3D Part Segmentation Point Cloud Segmentation +1

MisMatch: Calibrated Segmentation via Consistency on Differential Morphological Feature Perturbations with Limited Labels

2 code implementations23 Oct 2021 Mou-Cheng Xu, Yukun Zhou, Chen Jin, Marius de Groot, Neil P. Oxtoby, Daniel C. Alexander, Joseph Jacob

The state-of-the-art SSL methods in image classification utilise consistency regularisation to learn unlabelled predictions which are invariant to input level perturbations.

Image Classification Image Segmentation +4

Learning to Downsample for Segmentation of Ultra-High Resolution Images

1 code implementation ICLR 2022 Chen Jin, Ryutaro Tanno, Thomy Mertzanidou, Eleftheria Panagiotaki, Daniel C. Alexander

Many computer vision systems require low-cost segmentation algorithms based on deep learning, either because of the enormous size of input images or limited computational budget.

Segmentation Vocal Bursts Intensity Prediction

Learning to Address Intra-segment Misclassification in Retinal Imaging

2 code implementations25 Apr 2021 Yukun Zhou, MouCheng Xu, Yipeng Hu, Hongxiang Lin, Joseph Jacob, Pearse A. Keane, Daniel C. Alexander

Accurate multi-class segmentation is a long-standing challenge in medical imaging, especially in scenarios where classes share strong similarity.

Retinal Vessel Segmentation Segmentation

Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrast

1 code implementation24 Dec 2020 Juan Eugenio Iglesias, Benjamin Billot, Yael Balbastre, Azadeh Tabari, John Conklin, Daniel C. Alexander, Polina Golland, Brian L. Edlow, Bruce Fischl

Most existing algorithms for automatic 3D morphometry of human brain MRI scans are designed for data with near-isotropic voxels at approximately 1 mm resolution, and frequently have contrast constraints as well - typically requiring T1 scans (e. g., MP-RAGE).

Image Registration Skull Stripping +1

Learning transition times in event sequences: the Event-Based Hidden Markov Model of disease progression

no code implementations2 Nov 2020 Peter A. Wijeratne, Daniel C. Alexander

We use clinical, imaging and biofluid data from the Alzheimer's Disease Neuroimaging Initiative to demonstrate the validity and utility of our model.

Learning To Pay Attention To Mistakes

1 code implementation29 Jul 2020 Mou-Cheng Xu, Neil P. Oxtoby, Daniel C. Alexander, Joseph Jacob

We compared our methods with state-of-the-art attention mechanisms in medical imaging, including self-attention, spatial-attention and spatial-channel mixed attention.

Image Segmentation Medical Image Segmentation +1

Foveation for Segmentation of Ultra-High Resolution Images

1 code implementation29 Jul 2020 Chen Jin, Ryutaro Tanno, Mou-Cheng Xu, Thomy Mertzanidou, Daniel C. Alexander

We demonstrate on three publicly available high-resolution image datasets that the foveation module consistently improves segmentation performance over the cases trained with patches of fixed FoV/resolution trade-off.

Foveation Segmentation +1

Image Quality Transfer Enhances Contrast and Resolution of Low-Field Brain MRI in African Paediatric Epilepsy Patients

no code implementations16 Mar 2020 Matteo Figini, Hongxiang Lin, Godwin Ogbole, Felice D Arco, Stefano B. Blumberg, David W. Carmichael, Ryutaro Tanno, Enrico Kaden, Biobele J. Brown, Ikeoluwa Lagunju, Helen J. Cross, Delmiro Fernandez-Reyes, Daniel C. Alexander

1. 5T or 3T scanners are the current standard for clinical MRI, but low-field (<1T) scanners are still common in many lower- and middle-income countries for reasons of cost and robustness to power failures.

Management

The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

4 code implementations9 Feb 2020 Razvan V. Marinescu, Neil P. Oxtoby, Alexandra L. Young, Esther E. Bron, Arthur W. Toga, Michael W. Weiner, Frederik Barkhof, Nick C. Fox, Arman Eshaghi, Tina Toni, Marcin Salaterski, Veronika Lunina, Manon Ansart, Stanley Durrleman, Pascal Lu, Samuel Iddi, Dan Li, Wesley K. Thompson, Michael C. Donohue, Aviv Nahon, Yarden Levy, Dan Halbersberg, Mariya Cohen, Huiling Liao, Tengfei Li, Kaixian Yu, Hongtu Zhu, Jose G. Tamez-Pena, Aya Ismail, Timothy Wood, Hector Corrada Bravo, Minh Nguyen, Nanbo Sun, Jiashi Feng, B. T. Thomas Yeo, Gang Chen, Ke Qi, Shiyang Chen, Deqiang Qiu, Ionut Buciuman, Alex Kelner, Raluca Pop, Denisa Rimocea, Mostafa M. Ghazi, Mads Nielsen, Sebastien Ourselin, Lauge Sorensen, Vikram Venkatraghavan, Keli Liu, Christina Rabe, Paul Manser, Steven M. Hill, James Howlett, Zhiyue Huang, Steven Kiddle, Sach Mukherjee, Anais Rouanet, Bernd Taschler, Brian D. M. Tom, Simon R. White, Noel Faux, Suman Sedai, Javier de Velasco Oriol, Edgar E. V. Clemente, Karol Estrada, Leon Aksman, Andre Altmann, Cynthia M. Stonnington, Yalin Wang, Jianfeng Wu, Vivek Devadas, Clementine Fourrier, Lars Lau Raket, Aristeidis Sotiras, Guray Erus, Jimit Doshi, Christos Davatzikos, Jacob Vogel, Andrew Doyle, Angela Tam, Alex Diaz-Papkovich, Emmanuel Jammeh, Igor Koval, Paul Moore, Terry J. Lyons, John Gallacher, Jussi Tohka, Robert Ciszek, Bruno Jedynak, Kruti Pandya, Murat Bilgel, William Engels, Joseph Cole, Polina Golland, Stefan Klein, Daniel C. Alexander

TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease.

Alzheimer's Disease Detection Disease Prediction

Degenerative Adversarial NeuroImage Nets for Brain Scan Simulations: Application in Ageing and Dementia

no code implementations3 Dec 2019 Daniele Ravi, Stefano B. Blumberg, Silvia Ingala, Frederik Barkhof, Daniel C. Alexander, Neil P. Oxtoby

To evaluate our approach, we trained the framework on 9852 T1-weighted MRI scans from 876 participants in the Alzheimer's Disease Neuroimaging Initiative dataset and held out a separate test set of 1283 MRI scans from 170 participants for quantitative and qualitative assessment of the personalised time series of synthetic images.

Image Quality Assessment Super-Resolution +3

Multi-Stage Prediction Networks for Data Harmonization

no code implementations26 Jul 2019 Stefano B. Blumberg, Marco Palombo, Can Son Khoo, Chantal M. W. Tax, Ryutaro Tanno, Daniel C. Alexander

Specifically, we introduce the Multi Stage Prediction (MSP) Network, a MTL framework that incorporates neural networks of potentially disparate architectures, trained for different individual acquisition platforms, into a larger architecture that is refined in unison.

Multi-Task Learning

Degenerative Adversarial NeuroImage Nets: Generating Images that Mimic Disease Progression

no code implementations5 Jul 2019 Daniele Ravi, Daniel C. Alexander, Neil P. Oxtoby

Simulating images representative of neurodegenerative diseases is important for predicting patient outcomes and for validation of computational models of disease progression.

Predicting Patient Outcomes

BrainPainter: A software for the visualisation of brain structures, biomarkers and associated pathological processes

2 code implementations21 May 2019 Razvan V. Marinescu, Arman Eshaghi, Daniel C. Alexander, Polina Golland

Compared to existing visualisation software (i. e. Freesurfer, SPM, 3D Slicer), BrainPainter has three key advantages: (1) it does not require the input data to be in a specialised format, allowing BrainPainter to be used in combination with any neuroimaging analysis tools, (2) it can visualise both cortical and subcortical structures and (3) it can be used to generate movies showing dynamic processes, e. g. propagation of pathology on the brain.

Graphics Image and Video Processing

Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion

1 code implementation CVPR 2019 Ryutaro Tanno, Ardavan Saeedi, Swami Sankaranarayanan, Daniel C. Alexander, Nathan Silberman

We provide a theoretical argument as to how the regularization is essential to our approach both for the case of single annotator and multiple annotators.

Image Classification

Disease Knowledge Transfer across Neurodegenerative Diseases

2 code implementations11 Jan 2019 Razvan V. Marinescu, Marco Lorenzi, Stefano B. Blumberg, Alexandra L. Young, Pere P. Morell, Neil P. Oxtoby, Arman Eshaghi, Keir X. Yong, Sebastian J. Crutch, Polina Golland, Daniel C. Alexander

DKT infers robust multimodal biomarker trajectories in rare neurodegenerative diseases even when only limited, unimodal data is available, by transferring information from larger multimodal datasets from common neurodegenerative diseases.

Transfer Learning

Deeper Image Quality Transfer: Training Low-Memory Neural Networks for 3D Images

1 code implementation16 Aug 2018 Stefano B. Blumberg, Ryutaro Tanno, Iasonas Kokkinos, Daniel C. Alexander

In this paper we address the memory demands that come with the processing of 3-dimensional, high-resolution, multi-channeled medical images in deep learning.

Adaptive Neural Trees

1 code implementation ICLR 2019 Ryutaro Tanno, Kai Arulkumaran, Daniel C. Alexander, Antonio Criminisi, Aditya Nori

Deep neural networks and decision trees operate on largely separate paradigms; typically, the former performs representation learning with pre-specified architectures, while the latter is characterised by learning hierarchies over pre-specified features with data-driven architectures.

General Classification Representation Learning

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