Search Results for author:
Found 271 papers, 118 papers with code
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
This means that the super-resolution (SR) operation is performed in HR space.
Ranked #1 on
Video Super-Resolution
on Xiph HD - 4x upscaling
A generic framework for privacy preserving deep learning
We detail a new framework for privacy preserving deep learning and discuss its assets.
Attention U-Net: Learning Where to Look for the Pancreas
We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes.
Ranked #1 on
Pancreas Segmentation
on CT-150
Attention-Gated Networks for Improving Ultrasound Scan Plane Detection
We show that, when the base network has a high capacity, the incorporated attention mechanism can provide efficient object localisation while improving the overall performance.
DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images
We present DLTK, a toolkit providing baseline implementations for efficient experimentation with deep learning methods on biomedical images.
Domain Adaptation for MRI Organ Segmentation using Reverse Classification Accuracy
The variations in multi-center data in medical imaging studies have brought the necessity of domain adaptation.
Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation
We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation.
Ranked #1 on
Lesion Segmentation
on ISLES-2015
3D Medical Imaging Segmentation
Brain Lesion Segmentation From Mri
+3
Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation
Few-shot semantic segmentation (FSS) has great potential for medical imaging applications.
Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction
In particular, the proposed architecture embeds the structure of the traditional iterative algorithms, efficiently modelling the recurrence of the iterative reconstruction stages by using recurrent hidden connections over such iterations.
A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction
Firstly, we show that when each 2D image frame is reconstructed independently, the proposed method outperforms state-of-the-art 2D compressed sensing approaches such as dictionary learning-based MR image reconstruction, in terms of reconstruction error and reconstruction speed.
A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction
The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow.
Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks
Evaluating similarity between graphs is of major importance in several computer vision and pattern recognition problems, where graph representations are often used to model objects or interactions between elements.
Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Disease
Graphs are widely used as a natural framework that captures interactions between individual elements represented as nodes in a graph.
Spectral Graph Convolutions for Population-based Disease Prediction
We demonstrate the potential of the method on the challenging ADNI and ABIDE databases, as a proof of concept of the benefit from integrating contextual information in classification tasks.
Automated cardiovascular magnetic resonance image analysis with fully convolutional networks
By combining FCN with a large-scale annotated dataset, the proposed automated method achieves a high performance on par with human experts in segmenting the LV and RV on short-axis CMR images and the left atrium (LA) and right atrium (RA) on long-axis CMR images.
NeuroNet: Fast and Robust Reproduction of Multiple Brain Image Segmentation Pipelines
NeuroNet is a deep convolutional neural network mimicking multiple popular and state-of-the-art brain segmentation tools including FSL, SPM, and MALPEM.
Interactive and Explainable Region-guided Radiology Report Generation
While previous methods generate reports without the possibility of human intervention and with limited explainability, our method opens up novel clinical use cases through additional interactive capabilities and introduces a high degree of transparency and explainability.
Ranked #1 on
Medical Report Generation
on MIMIC-CXR
Physiology-based simulation of the retinal vasculature enables annotation-free segmentation of OCT angiographs
Encouraged by our method's competitive quantitative and superior qualitative performance, we believe that it constitutes a versatile tool to advance the quantitative analysis of OCTA images.
Deep learning cardiac motion analysis for human survival prediction
Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images.
Communicative Reinforcement Learning Agents for Landmark Detection in Brain Images
Accurate detection of anatomical landmarks is an essential step in several medical imaging tasks.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Causality-inspired Single-source Domain Generalization for Medical Image Segmentation
In this work, we investigate the single-source domain generalization problem: training a deep network that is robust to unseen domains, under the condition that training data is only available from one source domain, which is common in medical imaging applications.
Best of Both Worlds: Multimodal Contrastive Learning with Tabular and Imaging Data
Medical datasets and especially biobanks, often contain extensive tabular data with rich clinical information in addition to images.
PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI
In this paper we present a novel method for the correction of motion artifacts that are present in fetal Magnetic Resonance Imaging (MRI) scans of the whole uterus.
Multiple Landmark Detection using Multi-Agent Reinforcement Learning
We compare our approach with state-of-the-art architectures and achieve significantly better accuracy by reducing the detection error by 50%, while requiring fewer computational resources and time to train compared to the naive approach of training K agents separately.
Fast Multiple Landmark Localisation Using a Patch-based Iterative Network
PIN is computationally efficient since the inference stage only selectively samples a small number of patches in an iterative fashion rather than a dense sampling at every location in the volume.
Realistic Adversarial Data Augmentation for MR Image Segmentation
In this work, we propose an adversarial data augmentation method for training neural networks for medical image segmentation.
Enhancing MR Image Segmentation with Realistic Adversarial Data Augmentation
The success of neural networks on medical image segmentation tasks typically relies on large labeled datasets for model training.
$Σ$-net: Systematic Evaluation of Iterative Deep Neural Networks for Fast Parallel MR Image Reconstruction
Purpose: To systematically investigate the influence of various data consistency layers, (semi-)supervised learning and ensembling strategies, defined in a $\Sigma$-net, for accelerated parallel MR image reconstruction using deep learning.
Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences
Cardiac motion estimation and segmentation play important roles in quantitatively assessing cardiac function and diagnosing cardiovascular diseases.
Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach
The proposed pipeline is fully automated, due to network's ability to infer landmarks, which are then used downstream in the pipeline to initialise atlas propagation.
dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance
AUTOMAP is a promising generalized reconstruction approach, however, it is not scalable and hence the practicality is limited.
VS-Net: Variable splitting network for accelerated parallel MRI reconstruction
In this work, we propose a deep learning approach for parallel magnetic resonance imaging (MRI) reconstruction, termed a variable splitting network (VS-Net), for an efficient, high-quality reconstruction of undersampled multi-coil MR data.
k-t NEXT: Dynamic MR Image Reconstruction Exploiting Spatio-temporal Correlations
Experiments conducted on highly undersampled short-axis cardiac cine MRI scans demonstrate that our proposed method outperforms the current state-of-the-art dynamic MR reconstruction approaches both quantitatively and qualitatively.
Complementary Time-Frequency Domain Networks for Dynamic Parallel MR Image Reconstruction
The iterative model is embedded into a deep recurrent neural network which learns to recover the image via exploiting spatio-temporal redundancies in complementary domains.
Cooperative Training and Latent Space Data Augmentation for Robust Medical Image Segmentation
In this paper, we present a cooperative framework for training image segmentation models and a latent space augmentation method for generating hard examples.
MaxStyle: Adversarial Style Composition for Robust Medical Image Segmentation
We propose a novel data augmentation framework called MaxStyle, which maximizes the effectiveness of style augmentation for model OOD performance.
Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction
In addition, we introduce a semantic interpretability score, measuring the visibility of the region of interest in both ground truth and reconstructed images, which allows us to objectively quantify the usefulness of the image quality for image post-processing and analysis.
Joint Learning of Localized Representations from Medical Images and Reports
Contrastive learning has proven effective for pre-training image models on unlabeled data with promising results for tasks such as medical image classification.
Surface Vision Transformers: Attention-Based Modelling applied to Cortical Analysis
Motivated by the success of attention-modelling in computer vision, we translate convolution-free vision transformer approaches to surface data, to introduce a domain-agnostic architecture to study any surface data projected onto a spherical manifold.
Surface Vision Transformers: Flexible Attention-Based Modelling of Biomedical Surfaces
Results suggest that Surface Vision Transformers (SiT) demonstrate consistent improvement over geometric deep learning methods for brain age and fluid intelligence prediction and achieve comparable performance on calcium score classification to standard metrics used in clinical practice.
Surface Analysis with Vision Transformers
The extension of convolutional neural networks (CNNs) to non-Euclidean geometries has led to multiple frameworks for studying manifolds.
The Multiscale Surface Vision Transformer
Surface meshes are a favoured domain for representing structural and functional information on the human cortex, but their complex topology and geometry pose significant challenges for deep learning analysis.
Surface Masked AutoEncoder: Self-Supervision for Cortical Imaging Data
By reconstructing surface data from a masked version of the input, the proposed method effectively models cortical structure to learn strong representations that translate to improved performance in downstream tasks.
SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound
In this paper, we propose a novel method based on convolutional neural networks which can automatically detect 13 fetal standard views in freehand 2D ultrasound data as well as provide a localisation of the fetal structures via a bounding box.
Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network
We propose a new Iterative Transformation Network (ITN) for the automatic detection of standard planes in 3D volumes.
Multi-Task Learning for Left Atrial Segmentation on GE-MRI
Segmentation of the left atrium (LA) is crucial for assessing its anatomy in both pre-operative atrial fibrillation (AF) ablation planning and post-operative follow-up studies.
Ultrasound Video Summarization using Deep Reinforcement Learning
We show that our method is superior to alternative video summarization methods and that it preserves essential information required by clinical diagnostic standards.
CortexODE: Learning Cortical Surface Reconstruction by Neural ODEs
Following the isosurface extraction step, two CortexODE models are trained to deform the initial surface to white matter and pial surfaces respectively.
Unsupervised Pathology Detection: A Deep Dive Into the State of the Art
Our experiments demonstrate that newly developed feature-modeling methods from the industrial and medical literature achieve increased performance compared to previous work and set the new SOTA in a variety of modalities and datasets.
Biomechanics-informed Neural Networks for Myocardial Motion Tracking in MRI
The learnt VAE regulariser then can be coupled with any deep learning based registration network to regularise the solution space to be biomechanically plausible.
Denoising diffusion-based MRI to CT image translation enables automated spinal segmentation
The 3D translation outperformed the 2D approach, resulting in improved Dice scores (0. 80) and anatomically accurate segmentations in a higher resolution than the original MR image.
A skeletonization algorithm for gradient-based optimization
Finally, we demonstrate the utility of our algorithm by integrating it with two medical image processing applications that use gradient-based optimization: deep-learning-based blood vessel segmentation, and multimodal registration of the mandible in computed tomography and magnetic resonance images.
Computing CNN Loss and Gradients for Pose Estimation with Riemannian Geometry
In this paper, we propose a general Riemannian formulation of the pose estimation problem.
Predicting Slice-to-Volume Transformation in Presence of Arbitrary Subject Motion
Our approach is attractive in challenging imaging scenarios, where significant subject motion complicates reconstruction performance of 3D volumes from 2D slice data.
The Brain Tumor Segmentation (BraTS) Challenge 2023: Local Synthesis of Healthy Brain Tissue via Inpainting
The challenge is organized as part of the BraTS 2023 challenge hosted at the MICCAI 2023 conference in Vancouver, Canada.
Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images
AGs can be easily integrated into standard CNN models such as VGG or U-Net architectures with minimal computational overhead while increasing the model sensitivity and prediction accuracy.
Improving Phenotype Prediction using Long-Range Spatio-Temporal Dynamics of Functional Connectivity
Results show a prediction accuracy of 94. 4% for sex classification (an increase of 6. 2% compared to other methods), and an improvement of correlation with fluid intelligence of 0. 325 vs 0. 144, relative to a baseline model that encodes space and time separately.
Single-subject Multi-contrast MRI Super-resolution via Implicit Neural Representations
Clinical routine and retrospective cohorts commonly include multi-parametric Magnetic Resonance Imaging; however, they are mostly acquired in different anisotropic 2D views due to signal-to-noise-ratio and scan-time constraints.
Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations
To address this issue, recent work has employed transfer learning, where a segmentation network is trained on synthetic OCTA images and is then applied to real data.
PialNN: A Fast Deep Learning Framework for Cortical Pial Surface Reconstruction
Traditional cortical surface reconstruction is time consuming and limited by the resolution of brain Magnetic Resonance Imaging (MRI).
Link Prediction for Flow-Driven Spatial Networks
In this work, we focus on link prediction for flow-driven spatial networks, which are embedded in a Euclidean space and relate to physical exchange and transportation processes (e. g., blood flow in vessels or traffic flow in road networks).
HyFed: A Hybrid Federated Framework for Privacy-preserving Machine Learning
Federated learning (FL) enables multiple clients to jointly train a global model under the coordination of a central server.
Unsupervised Anomaly Localization with Structural Feature-Autoencoders
Most commonly, the anomaly detection model generates a "normal" version of an input image, and the pixel-wise $l^p$-difference of the two is used to localize anomalies.
NISF: Neural Implicit Segmentation Functions
Approaches that rely on convolutional neural networks (CNNs) are limited to grid-like inputs and not easily applicable to sparse or partial measurements.
Towards Universal Unsupervised Anomaly Detection in Medical Imaging
Our unsupervised anomaly detection approach may enhance diagnostic accuracy in medical imaging by identifying a broader range of unknown pathologies.
Geometric Deep Learning for Post-Menstrual Age Prediction based on the Neonatal White Matter Cortical Surface
In this paper, we propose a novel approach to predict the post-menstrual age (PA) at scan, using techniques from geometric deep learning, based on the neonatal white matter cortical surface.
Panoptica -- instance-wise evaluation of 3D semantic and instance segmentation maps
This paper introduces panoptica, a versatile and performance-optimized package designed for computing instance-wise segmentation quality metrics from 2D and 3D segmentation maps.
Differentially Private Graph Classification with GNNs
In this work, we introduce differential privacy for graph-level classification, one of the key applications of machine learning on graphs.
CHeart: A Conditional Spatio-Temporal Generative Model for Cardiac Anatomy
Two key questions in cardiac image analysis are to assess the anatomy and motion of the heart from images; and to understand how they are associated with non-imaging clinical factors such as gender, age and diseases.
A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging
Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) widely used for visualizing diseased cardiac structures, is a crucial first step for clinical diagnosis and treatment.
Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRI
In this work, we tackle the problem of Semi-Supervised Anomaly Segmentation (SAS) in Magnetic Resonance Images (MRI) of the brain, which is the task of automatically identifying pathologies in brain images.
Generative Myocardial Motion Tracking via Latent Space Exploration with Biomechanics-informed Prior
In contrast to most existing approaches which impose explicit generic regularization such as smoothness, in this work we propose a novel method that can implicitly learn an application-specific biomechanics-informed prior and embed it into a neural network-parameterized transformation model.
Conditional Temporal Attention Networks for Neonatal Cortical Surface Reconstruction
The importance of each SVF, which is estimated by learned attention maps, is conditioned on the age of the neonates and varies with the time step of integration.
Unlocking the Diagnostic Potential of ECG through Knowledge Transfer from Cardiac MRI
In a qualitative analysis, we demonstrate that our learned ECG embeddings incorporate information from CMR image regions of interest.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Differentially private federated deep learning for multi-site medical image segmentation
The application of PTs to FL in medical imaging and the trade-offs between privacy guarantees and model utility, the ramifications on training performance and the susceptibility of the final models to attacks have not yet been conclusively investigated.
Robust Detection Outcome: A Metric for Pathology Detection in Medical Images
To tackle this problem, we propose Robust Detection Outcome (RoDeO); a novel metric for evaluating algorithms for pathology detection in medical images, especially in chest X-rays.
Multimodal brain age estimation using interpretable adaptive population-graph learning
We further show that the assigned attention scores indicate that there are both imaging and non-imaging phenotypes that are informative for brain age estimation and are in agreement with the relevant literature.
Improving the generalizability of convolutional neural network-based segmentation on CMR images
We demonstrate that a neural network trained on a single-site single-scanner dataset from the UK Biobank can be successfully applied to segmenting cardiac MR images across different sites and different scanners without substantial loss of accuracy.
3D Arterial Segmentation via Single 2D Projections and Depth Supervision in Contrast-Enhanced CT Images
In this work, we propose a novel method to segment the 3D peripancreatic arteries solely from one annotated 2D projection per training image with depth supervision.
Detecting Outliers with Poisson Image Interpolation
We propose an alternative to image reconstruction-based and image embedding-based methods and propose a new self-supervised method to tackle pathological anomaly detection.
Mask, Stitch, and Re-Sample: Enhancing Robustness and Generalizability in Anomaly Detection through Automatic Diffusion Models
The introduction of diffusion models in anomaly detection has paved the way for more effective and accurate image reconstruction in pathologies.
AutoSeg -- Steering the Inductive Biases for Automatic Pathology Segmentation
In medical imaging, un-, semi-, or self-supervised pathology detection is often approached with anomaly- or out-of-distribution detection methods, whose inductive biases are not intentionally directed towards detecting pathologies, and are therefore sub-optimal for this task.
On the Pitfalls of Using the Residual Error as Anomaly Score
Many current state-of-the-art methods for anomaly localization in medical images rely on calculating a residual image between a potentially anomalous input image and its "healthy" reconstruction.
SmoothNets: Optimizing CNN architecture design for differentially private deep learning
The arguably most widely employed algorithm to train deep neural networks with Differential Privacy is DPSGD, which requires clipping and noising of per-sample gradients.
Ranked #2 on
Image Classification
on Imagenette
Unsupervised Cross-domain Image Classification by Distance Metric Guided Feature Alignment
The proposed MetFA method explicitly and directly learns the latent representation without using domain adversarial training.
Personalized Federated Deep Learning for Pain Estimation From Face Images
A potential approach to tackling this is Federated Learning (FL), which enables multiple parties to collaboratively learn a shared prediction model by using parameters of locally trained models while keeping raw training data locally.
Embedding Gradient-based Optimization in Image Registration Networks
Deep learning (DL) image registration methods amortize the costly pair-wise iterative optimization by training deep neural networks to predict the optimal transformation in one fast forward-pass.
Placenta Segmentation in Ultrasound Imaging: Addressing Sources of Uncertainty and Limited Field-of-View
Automatic segmentation of the placenta in fetal ultrasound (US) is challenging due to the (i) high diversity of placenta appearance, (ii) the restricted quality in US resulting in highly variable reference annotations, and (iii) the limited field-of-view of US prohibiting whole placenta assessment at late gestation.
Generative Modelling of the Ageing Heart with Cross-Sectional Imaging and Clinical Data
Understanding the morphological and functional changes of the heart during ageing is a key scientific question, the answer to which will help us define important risk factors of cardiovascular disease and monitor disease progression.
Global k-Space Interpolation for Dynamic MRI Reconstruction using Masked Image Modeling
We evaluate our approach on 92 in-house 2D+t cardiac MR subjects and compare it to MR reconstruction methods with image-domain regularizers.
Anatomy-Driven Pathology Detection on Chest X-rays
Pathology detection and delineation enables the automatic interpretation of medical scans such as chest X-rays while providing a high level of explainability to support radiologists in making informed decisions.
Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging
In this work, we evaluated the effect of privacy-preserving training of AI models regarding accuracy and fairness compared to non-private training.
Interpretable 2D Vision Models for 3D Medical Images
Training Artificial Intelligence (AI) models on 3D images presents unique challenges compared to the 2D case: Firstly, the demand for computational resources is significantly higher, and secondly, the availability of large datasets for pre-training is often limited, impeding training success.
Self-pruning Graph Neural Network for Predicting Inflammatory Disease Activity in Multiple Sclerosis from Brain MR Images
We propose a two-stage MS inflammatory disease activity prediction approach.
SPINEPS -- Automatic Whole Spine Segmentation of T2-weighted MR images using a Two-Phase Approach to Multi-class Semantic and Instance Segmentation
Training on auto-generated annotations and evaluating on manually corrected test data from the GNC yielded global dice scores of 0. 900 for vertebrae, 0. 960 for intervertebral discs, and 0. 947 for the spinal canal.
Diffusion Models with Implicit Guidance for Medical Anomaly Detection
Diffusion models have advanced unsupervised anomaly detection by improving the transformation of pathological images into pseudo-healthy equivalents.
Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation
However, in most recent and promising techniques such as CNN based segmentation it is not obvious how to incorporate such prior knowledge.
Representation Disentanglement for Multi-task Learning with application to Fetal Ultrasound
These entangled image properties lead to a semantically redundant feature encoding for the relevant task and thus lead to poor generalization of deep learning algorithms.
A deep learning approach to segmentation of the developing cortex in fetal brain MRI with minimal manual labeling
We developed an automated system based on deep neural networks for fast and sensitive 3D image segmentation of cortical gray matter from fetal brain MRI.
Weakly Supervised Object Detection in Chest X-Rays with Differentiable ROI Proposal Networks and Soft ROI Pooling
Weakly supervised object detection (WSup-OD) increases the usefulness and interpretability of image classification algorithms without requiring additional supervision.
Efficient, high-performance pancreatic segmentation using multi-scale feature extraction
For artificial intelligence-based image analysis methods to reach clinical applicability, the development of high-performance algorithms is crucial.
Training deep segmentation networks on texture-encoded input: application to neuroimaging of the developing neonatal brain
Standard practice for using convolutional neural networks (CNNs) in semantic segmentation tasks assumes that the image intensities are directly used for training and inference.
Improved post-hoc probability calibration for out-of-domain MRI segmentation
In image segmentation, well-calibrated probabilities allow radiologists to identify regions where model-predicted segmentations are unreliable.
Physics-Aware Motion Simulation for T2*-Weighted Brain MRI
As T2*-weighted MRI is highly sensitive to motion-related changes in magnetic field inhomogeneities, it is of utmost importance to include physics information in the simulation.
A Comparative Study of Population-Graph Construction Methods and Graph Neural Networks for Brain Age Regression
We conclude that static graph construction approaches are potentially insufficient for the task of brain age estimation and make recommendations for alternative research directions.
TopCoW: Benchmarking Topology-Aware Anatomical Segmentation of the Circle of Willis (CoW) for CTA and MRA
The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology.
Intensity-based 3D motion correction for cardiac MR images
Cardiac magnetic resonance (CMR) image acquisition requires subjects to hold their breath while 2D cine images are acquired.
Real-time Prediction of Segmentation Quality
Recent advances in deep learning based image segmentation methods have enabled real-time performance with human-level accuracy.
Automatic View Planning with Multi-scale Deep Reinforcement Learning Agents
Navigating through target anatomy to find the required view plane is tedious and operator-dependent.
3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation
We estimate the position and the size of the pancreas (localized) from global features by regression forests.
Semi-Supervised Learning via Compact Latent Space Clustering
We present a novel cost function for semi-supervised learning of neural networks that encourages compact clustering of the latent space to facilitate separation.
Graph Saliency Maps through Spectral Convolutional Networks: Application to Sex Classification with Brain Connectivity
Graph convolutional networks (GCNs) allow to apply traditional convolution operations in non-Euclidean domains, where data are commonly modelled as irregular graphs.
Stochastic Deep Compressive Sensing for the Reconstruction of Diffusion Tensor Cardiac MRI
To the best of our knowledge, this is the first study using deep CNN based CS for the DT-CMR reconstruction.
Human-level Performance On Automatic Head Biometrics In Fetal Ultrasound Using Fully Convolutional Neural Networks
Measurement of head biometrics from fetal ultrasonography images is of key importance in monitoring the healthy development of fetuses.
Learning-Based Quality Control for Cardiac MR Images
The results show the capability of the proposed pipeline to correctly detect incomplete or corrupted scans (e. g. on UK Biobank, sensitivity and specificity respectively 88% and 99% for heart coverage estimation, 85% and 95% for motion detection), allowing their exclusion from the analysed dataset or the triggering of a new acquisition.
3D Reconstruction in Canonical Co-ordinate Space from Arbitrarily Oriented 2D Images
We extensively evaluate the effectiveness of our approach quantitatively on simulated Magnetic Resonance Imaging (MRI), fetal brain imagery with synthetic motion and further demonstrate qualitative results on real fetal MRI data where our method is integrated into a full reconstruction and motion compensation pipeline.
Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation
Deep learning approaches such as convolutional neural nets have consistently outperformed previous methods on challenging tasks such as dense, semantic segmentation.
Is human face processing a feature- or pattern-based task? Evidence using a unified computational method driven by eye movements
Research on human face processing using eye movements has provided evidence that we recognize face images successfully focusing our visual attention on a few inner facial regions, mainly on the eyes, nose and mouth.
Facial Expression Recognition
Facial Expression Recognition (FER)
Employing Weak Annotations for Medical Image Analysis Problems
To efficiently establish training databases for machine learning methods, collaborative and crowdsourcing platforms have been investigated to collectively tackle the annotation effort.
Exploring Heritability of Functional Brain Networks with Inexact Graph Matching
Data-driven brain parcellations aim to provide a more accurate representation of an individual's functional connectivity, since they are able to capture individual variability that arises due to development or disease.
Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth
In RCA we take the predicted segmentation from a new image to train a reverse classifier which is evaluated on a set of reference images with available ground truth.
Unsupervised domain adaptation in brain lesion segmentation with adversarial networks
In this work we investigate unsupervised domain adaptation using adversarial neural networks to train a segmentation method which is more invariant to differences in the input data, and which does not require any annotations on the test domain.
Comparison of Brain Networks with Unknown Correspondences
In this work we explore a method based on graph edit distance for evaluating graph similarity, when correspondences between network elements are unknown due to different underlying subdivisions of the brain.
DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks
In this paper, we propose DeepCut, a method to obtain pixelwise object segmentations given an image dataset labelled with bounding box annotations.
Learning under Distributed Weak Supervision
The availability of training data for supervision is a frequently encountered bottleneck of medical image analysis methods.
Multi-Atlas Segmentation using Partially Annotated Data: Methods and Annotation Strategies
Multi-atlas segmentation is a widely used tool in medical image analysis, providing robust and accurate results by learning from annotated atlas datasets.
Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling
However, current approaches to the diagnosis of cardiovascular diseases often rely on subjective human assessment as well as manual analysis of medical images.
Small Organ Segmentation in Whole-body MRI using a Two-stage FCN and Weighting Schemes
Our experiments show that the proposed approach can boost the segmentation accuracy of multiple small organs in whole-body MRI scans.
Deep nested level sets: Fully automated segmentation of cardiac MR images in patients with pulmonary hypertension
In this paper we introduce a novel and accurate optimisation method for segmentation of cardiac MR (CMR) images in patients with pulmonary hypertension (PH).
Recurrent neural networks for aortic image sequence segmentation with sparse annotations
In this work, we propose an image sequence segmentation algorithm by combining a fully convolutional network with a recurrent neural network, which incorporates both spatial and temporal information into the segmentation task.
Deep Learning using K-space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection
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.
A Comprehensive Approach for Learning-based Fully-Automated Inter-slice Motion Correction for Short-Axis Cine Cardiac MR Image Stacks
If long axis (LA) images are available, PSMs are generated for them and combined to create the target PSM; if not, the target PSM is produced from the same stack using a 3D model trained from motion-free stacks.
GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks
One of the biggest issues facing the use of machine learning in medical imaging is the lack of availability of large, labelled datasets.
Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning
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.
Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging
Our method is more consistent than human annotation, and outperforms the state-of-the-art quantitatively in shadow segmentation and qualitatively in confidence estimation of shadow regions.
GANsfer Learning: Combining labelled and unlabelled data for GAN based data augmentation
We also show how a shift in domain of the training data from young and healthy towards older and more pathological examples leads to better segmentations of the latter cases, and that this leads to a significant improvement in the ability for the computed segmentations to stratify cases of AD.
Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review
With more than 300 papers included in this review, we reflect on the trends and challenges of the field of computational anatomy, the particularities of each anatomical region, and the potential of multi-organ analysis to increase the impact of 35 medical imaging applications on the future of healthcare.
Automated Quality Control in Image Segmentation: Application to the UK Biobank Cardiac MR Imaging Study
Methods: To overcome this challenge, we explore an approach for predicting segmentation quality based on Reverse Classification Accuracy, which enables us to discriminate between successful and failed segmentations on a per-cases basis.
Patch-based Evaluation of Image Segmentation
The ideal similarity measure should be unbiased to segmentations of different volume and complexity, and be able to quantify and visualise segmentation bias.
3D High-Resolution Cardiac Segmentation Reconstruction from 2D Views using Conditional Variational Autoencoders
Accurate segmentation of heart structures imaged by cardiac MR is key for the quantitative analysis of pathology.
Generalising Deep Learning MRI Reconstruction across Different Domains
We look into robustness of deep learning based MRI reconstruction when tested on unseen contrasts and organs.
Unsupervised Deformable Registration for Multi-Modal Images via Disentangled Representations
The proposed registration approach is then built on the factorized latent shape code, with the assumption that the intrinsic shape deformation existing in original image domain is preserved in this latent space.
Meta-Weighted Gaussian Process Experts for Personalized Forecasting of AD Cognitive Changes
We introduce a novel personalized Gaussian Process Experts (pGPE) model for predicting per-subject ADAS-Cog13 cognitive scores -- a significant predictor of Alzheimer's Disease (AD) in the cognitive domain -- over the future 6, 12, 18, and 24 months.
Detection and Correction of Cardiac MR Motion Artefacts during Reconstruction from K-space
In this paper, we propose a method to automatically detect and correct motion-related artefacts in CMR acquisitions during reconstruction from k-space data.
Explainable Anatomical Shape Analysis through Deep Hierarchical Generative Models
At the highest level of this hierarchy, a two-dimensional latent space is simultaneously optimised to discriminate distinct clinical conditions, enabling the direct visualisation of the classification space.
Data Efficient Unsupervised Domain Adaptation for Cross-Modality Image Segmentation
Deep unsupervised domain adaptation (UDA) aims to improve the performance of a deep neural network model on a target domain, using solely unlabelled target domain data and labelled source domain data.
Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction
In the recent years, convolutional neural networks have transformed the field of medical image analysis due to their capacity to learn discriminative image features for a variety of classification and regression tasks.
Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view Images
Cardiac MR image segmentation is essential for the morphological and functional analysis of the heart.
Assessing the Impact of Blood Pressure on Cardiac Function Using Interpretable Biomarkers and Variational Autoencoders
Maintaining good cardiac function for as long as possible is a major concern for healthcare systems worldwide and there is much interest in learning more about the impact of different risk factors on cardiac health.
Unsupervised Multi-modal Style Transfer for Cardiac MR Segmentation
In this work, we present a fully automatic method to segment cardiac structures from late-gadolinium enhanced (LGE) images without using labelled LGE data for training, but instead by transferring the anatomical knowledge and features learned on annotated balanced steady-state free precession (bSSFP) images, which are easier to acquire.
Joint Motion Estimation and Segmentation from Undersampled Cardiac MR Image
Accelerating the acquisition of magnetic resonance imaging (MRI) is a challenging problem, and many works have been proposed to reconstruct images from undersampled k-space data.
Flexible Conditional Image Generation of Missing Data with Learned Mental Maps
Real-world settings often do not allow acquisition of high-resolution volumetric images for accurate morphological assessment and diagnostic.
Model-Based and Data-Driven Strategies in Medical Image Computing
With the availability of large amounts of imaging data and machine learning (in particular deep learning) techniques, data-driven approaches have become more widespread for use in different tasks in reconstruction, analysis and interpretation.
Intelligent image synthesis to attack a segmentation CNN using adversarial learning
Our approach has three key features: 1) The generated adversarial examples exhibit anatomical variations (in form of deformations) as well as appearance perturbations; 2) The adversarial examples attack segmentation models so that the Dice scores decrease by a pre-specified amount; 3) The attack is not required to be specified beforehand.
Data consistency networks for (calibration-less) accelerated parallel MR image reconstruction
We show that the proposed approaches are competitive relative to the state of the art both quantitatively and qualitatively.
Joint analysis of clinical risk factors and 4D cardiac motion for survival prediction using a hybrid deep learning network
In this work, a novel approach is proposed for joint analysis of high dimensional time-resolved cardiac motion features obtained from segmented cardiac MRI and low dimensional clinical risk factors to improve survival prediction in heart failure.
Deep learning for cardiac image segmentation: A review
Deep learning has become the most widely used approach for cardiac image segmentation in recent years.
Learning Cross-domain Generalizable Features by Representation Disentanglement
Deep learning models exhibit limited generalizability across different domains.
Regression Forest-Based Atlas Localization and Direction Specific Atlas Generation for Pancreas Segmentation
This paper proposes a fully automated atlas-based pancreas segmentation method from CT volumes utilizing atlas localization by regression forest and atlas generation using blood vessel information.
Cardiac Segmentation on Late Gadolinium Enhancement MRI: A Benchmark Study from Multi-Sequence Cardiac MR Segmentation Challenge
In addition, the paired MS-CMR images could enable algorithms to combine the complementary information from the other sequences for the segmentation of LGE CMR.
3D Probabilistic Segmentation and Volumetry from 2D projection images
X-Ray imaging is quick, cheap and useful for front-line care assessment and intra-operative real-time imaging (e. g., C-Arm Fluoroscopy).
Deep Generative Model-based Quality Control for Cardiac MRI Segmentation
Our approach provides a real-time and model-agnostic quality control for cardiac MRI segmentation, which has the potential to be integrated into clinical image analysis workflows.
Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction
Our key contribution is that the VAE disentangles the latent space based on `explanations' drawn from existing clinical knowledge.
Deep Network Interpolation for Accelerated Parallel MR Image Reconstruction
We present a deep network interpolation strategy for accelerated parallel MR image reconstruction.
Automated Detection of Congenital Heart Disease in Fetal Ultrasound Screening
In this paper we discuss the potential for deep learning techniques to aid in the detection of congenital heart disease (CHD) in fetal ultrasound.
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
In this survey paper, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in deep learning are addressing these issues.
Causal Future Prediction in a Minkowski Space-Time
In this paper we propose a novel theoretical framework to perform causal future prediction by embedding spatiotemporal information on a Minkowski space-time.
Patch-based Brain Age Estimation from MR Images
Many studies have been proposed for the prediction of chronological age from brain MRI using machine learning and specifically deep learning techniques.
Conditional GAN for Prediction of Glaucoma Progression with Macular Optical Coherence Tomography
The patient's OCT scan is predicted from three or two prior measurements.
Mutual Information-based Disentangled Neural Networks for Classifying Unseen Categories in Different Domains: Application to Fetal Ultrasound Imaging
To address this problem, we propose Mutual Information-based Disentangled Neural Networks (MIDNet), which extract generalizable categorical features to transfer knowledge to unseen categories in a target domain.
A Systematic Comparison of Encrypted Machine Learning Solutions for Image Classification
This work provides a comprehensive review of existing frameworks based on secure computing techniques in the context of private image classification.
2CP: Decentralized Protocols to Transparently Evaluate Contributivity in Blockchain Federated Learning Environments
While the initial model might belong solely to the actor bringing it to the network for training, determining the ownership of the trained model resulting from Federated Learning remains an open question.
Reducing Textural Bias Improves Robustness of Deep Segmentation Models
In this thorough empirical study, we draw inspiration from findings on natural images and investigate ways in which addressing the textural bias phenomenon could bring up the robustness of deep segmentation models when applied to three-dimensional (3D) medical data.
Robust Aggregation for Adaptive Privacy Preserving Federated Learning in Healthcare
In this work, we implement and evaluate different robust aggregation methods in FL applied to healthcare data.
Cryptography and Security
Privacy-preserving medical image analysis
The utilisation of artificial intelligence in medicine and healthcare has led to successful clinical applications in several domains.
FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentation
Further, we illustrate that FedDis learns a shape embedding that is orthogonal to the appearance and consistent under different intensity augmentations.
Automated Knee X-ray Report Generation
Gathering manually annotated images for the purpose of training a predictive model is far more challenging in the medical domain than for natural images as it requires the expertise of qualified radiologists.
Learning a Model-Driven Variational Network for Deformable Image Registration
We then propose two neural layers (i. e. warping layer and intensity consistency layer) to model the analytical solution and a residual U-Net to formulate the denoising problem (i. e. generalized denoising layer).
Video Summarization through Reinforcement Learning with a 3D Spatio-Temporal U-Net
Intelligent video summarization algorithms allow to quickly convey the most relevant information in videos through the identification of the most essential and explanatory content while removing redundant video frames.
Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-specific Atlas Maps
Fetal ultrasound screening during pregnancy plays a vital role in the early detection of fetal malformations which have potential long-term health impacts.
Joint Motion Correction and Super Resolution for Cardiac Segmentation via Latent Optimisation
In cardiac magnetic resonance (CMR) imaging, a 3D high-resolution segmentation of the heart is essential for detailed description of its anatomical structures.
Differentially private training of neural networks with Langevin dynamics for calibrated predictive uncertainty
We show that differentially private stochastic gradient descent (DP-SGD) can yield poorly calibrated, overconfident deep learning models.
Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation
Reconciling large-scale ML with the closed-form reasoning required for the principled analysis of individual privacy loss requires the introduction of new tools for automatic sensitivity analysis and for tracking an individual's data and their features through the flow of computation.
Joint Semi-supervised 3D Super-Resolution and Segmentation with Mixed Adversarial Gaussian Domain Adaptation
Optimising the analysis of cardiac structure and function requires accurate 3D representations of shape and motion.
Transductive image segmentation: Self-training and effect of uncertainty estimation
It focuses on the quality of predictions made on the unlabeled data of interest when they are included for optimization during training, rather than improving generalization.
NeuralDP Differentially private neural networks by design
The application of differential privacy to the training of deep neural networks holds the promise of allowing large-scale (decentralized) use of sensitive data while providing rigorous privacy guarantees to the individual.
Voxel-level Importance Maps for Interpretable Brain Age Estimation
In order to do so, we assume that voxels that are not useful for the regression are resilient to noise addition.
Partial sensitivity analysis in differential privacy
However, while techniques such as individual R\'enyi DP (RDP) allow for granular, per-person privacy accounting, few works have investigated the impact of each input feature on the individual's privacy loss.
An automatic differentiation system for the age of differential privacy
We introduce Tritium, an automatic differentiation-based sensitivity analysis framework for differentially private (DP) machine learning (ML).
A unified interpretation of the Gaussian mechanism for differential privacy through the sensitivity index
$\psi$ uniquely characterises the GM and its properties by encapsulating its two fundamental quantities: the sensitivity of the query and the magnitude of the noise perturbation.
DeepMCAT: Large-Scale Deep Clustering for Medical Image Categorization
In recent years, the research landscape of machine learning in medical imaging has changed drastically from supervised to semi-, weakly- or unsupervised methods.
Complex-valued deep learning with differential privacy
We present $\zeta$-DP, an extension of differential privacy (DP) to complex-valued functions.
FedRAD: Federated Robust Adaptive Distillation
The robustness of federated learning (FL) is vital for the distributed training of an accurate global model that is shared among large number of clients.
AI-based Reconstruction for Fast MRI -- A Systematic Review and Meta-analysis
Compressed sensing (CS) has been playing a key role in accelerating the magnetic resonance imaging (MRI) acquisition process.
Distributed Machine Learning and the Semblance of Trust
The utilisation of large and diverse datasets for machine learning (ML) at scale is required to promote scientific insight into many meaningful problems.
Multi-modal unsupervised brain image registration using edge maps
Diffeomorphic deformable multi-modal image registration is a challenging task which aims to bring images acquired by different modalities to the same coordinate space and at the same time to preserve the topology and the invertibility of the transformation.
Beyond Gradients: Exploiting Adversarial Priors in Model Inversion Attacks
Collaborative machine learning settings like federated learning can be susceptible to adversarial interference and attacks.
Differentially private training of residual networks with scale normalisation
The training of neural networks with Differentially Private Stochastic Gradient Descent offers formal Differential Privacy guarantees but introduces accuracy trade-offs.
SoK: Differential Privacy on Graph-Structured Data
In this work, we study the applications of differential privacy (DP) in the context of graph-structured data.
