Search Results for author: Daniel C. Alexander

Found 34 papers, 17 papers with code

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, Geoffrey JM Parker, Arman Eshaghi

To tackle this problem, we propose a novel framework having four main components: (1) a set of artefact generators inspired by magnetic resonance physics to corrupt brain MRI scans and augment a training dataset, (2) a set of abstract and engineered features to represent images compactly, (3) a feature selection process that depends on the class of artefact to improve classification performance, and (4) a set of Support Vector Machine (SVM) classifiers trained to identify artefacts.

Data Augmentation feature selection

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

1 code implementation21 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.

Progressive Subsampling for Oversampled Data -- Application to Quantitative MRI

no code implementations17 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.

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.

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

no 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

Learning to Downsample for Segmentation of Ultra-High Resolution Images

no code implementations 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.

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

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.

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.


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.

Medical Image Segmentation Semantic Segmentation

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.

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 +2

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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