Search Results for author:
Found 39 papers, 19 papers with code
Predicting risk of cardiovascular disease using retinal OCT imaging
A Random Forest (RF) classifier was subsequently trained using the learned latent features and participant demographic and clinical data, to differentiate between patients at risk of CVD events (MI or stroke) and non-CVD cases.
Source Matters: Source Dataset Impact on Model Robustness in Medical Imaging
However, the domain shift from natural to medical images has prompted alternatives such as RadImageNet, often demonstrating comparable classification performance.
Multi-view Hybrid Graph Convolutional Network for Volume-to-mesh Reconstruction in Cardiovascular MRI
Cardiovascular magnetic resonance imaging is emerging as a crucial tool to examine cardiac morphology and function.
Unsupervised bias discovery in medical image segmentation
It has recently been shown that deep learning models for anatomical segmentation in medical images can exhibit biases against certain sub-populations defined in terms of protected attributes like sex or ethnicity.
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare.
CheXmask: a large-scale dataset of anatomical segmentation masks for multi-center chest x-ray images
To address this gap, we introduce an extensive chest X-ray multi-center segmentation dataset with uniform and fine-grain anatomical annotations for images coming from five well-known publicly available databases: ChestX-ray8, Chexpert, MIMIC-CXR-JPG, Padchest, and VinDr-CXR, resulting in 657, 566 segmentation masks.
Towards unraveling calibration biases in medical image analysis
In recent years the development of artificial intelligence (AI) systems for automated medical image analysis has gained enormous momentum.
Are demographically invariant models and representations in medical imaging fair?
Medical imaging models have been shown to encode information about patient demographics such as age, race, and sex in their latent representation, raising concerns about their potential for discrimination.
Unsupervised ensemble-based phenotyping helps enhance the discoverability of genes related to heart morphology
Here, we propose a new framework for gene discovery entitled Unsupervised Phenotype Ensembles (UPE).
Multi-center anatomical segmentation with heterogeneous labels via landmark-based models
Learning anatomical segmentation from heterogeneous labels in multi-center datasets is a common situation encountered in clinical scenarios, where certain anatomical structures are only annotated in images coming from particular medical centers, but not in the full database.
Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis
To this end, we introduce HybridGNet, an encoder-decoder neural architecture that leverages standard convolutions for image feature encoding and graph convolutional neural networks (GCNNs) to decode plausible representations of anatomical structures.
Supervision by Denoising for Medical Image Segmentation
SUD unifies stochastic averaging and spatial denoising techniques under a spatio-temporal denoising framework and alternates denoising and model weight update steps in an optimization framework for semi-supervision.
Impact of class imbalance on chest x-ray classifiers: towards better evaluation practices for discrimination and calibration performance
This work aims to analyze standard evaluation practices adopted by the research community when assessing chest x-ray classifiers, particularly focusing on the impact of class imbalance in such appraisals.
Maximum Entropy on Erroneous Predictions (MEEP): Improving model calibration for medical image segmentation
Modern deep neural networks achieved remarkable progress in medical image segmentation tasks.
Domain Generalization via Gradient Surgery
In real-life applications, machine learning models often face scenarios where there is a change in data distribution between training and test domains.
Hybrid graph convolutional neural networks for landmark-based anatomical segmentation
In this work we address the problem of landmark-based segmentation for anatomical structures.
Bridging physiological and perceptual views of autism by means of sampling-based Bayesian inference
Theories for autism spectrum disorder (ASD) have been formulated at different levels: ranging from physiological observations to perceptual and behavioral descriptions.
Orthogonal Ensemble Networks for Biomedical Image Segmentation
Despite the astonishing performance of deep-learning based approaches for visual tasks such as semantic segmentation, they are known to produce miscalibrated predictions, which could be harmful for critical decision-making processes.
Cranial Implant Design via Virtual Craniectomy with Shape Priors
Our direct estimation method outperforms the baselines provided by the organizers, while the model with shape priors shows superior performance when dealing with out-of-distribution cases.
Unsupervised Domain Adaptation via CycleGAN for White Matter Hyperintensity Segmentation in Multicenter MR Images
When such distribution changes but we still aim at performing the same task, we incur in a domain adaptation problem (e. g. using a different MR machine or different acquisition parameters for training and test data).
Self-supervised Skull Reconstruction in Brain CT Images with Decompressive Craniectomy
Decompressive craniectomy (DC) is a common surgical procedure consisting of the removal of a portion of the skull that is performed after incidents such as stroke, traumatic brain injury (TBI) or other events that could result in acute subdural hemorrhage and/or increasing intracranial pressure.
Post-DAE: Anatomically Plausible Segmentation via Post-Processing with Denoising Autoencoders
We introduce Post-DAE, a post-processing method based on denoising autoencoders (DAE) to improve the anatomical plausibility of arbitrary biomedical image segmentation algorithms.
Learning Deformable Registration of Medical Images with Anatomical Constraints
Deformable image registration is a fundamental problem in the field of medical image analysis.
Anatomical Priors for Image Segmentation via Post-Processing with Denoising Autoencoders
We learn a low-dimensional space of anatomically plausible segmentations, and use it as a post-processing step to impose shape constraints on the resulting masks obtained with arbitrary segmentation methods.
Joint Learning of Brain Lesion and Anatomy Segmentation from Heterogeneous Datasets
However, to date, little work has been done regarding simultaneous learning of brain lesion and anatomy segmentation from disjoint datasets.
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.
Weakly-Supervised Learning of Metric Aggregations for Deformable Image Registration
Conventional approaches refer to the definition of a similarity criterion that, once endowed with a deformation model and a smoothness constraint, determines the optimal transformation to align two given images.
Left ventricle quantification through spatio-temporal CNNs
Cardiovascular diseases are among the leading causes of death globally.
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.
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.
Deformable Registration through Learning of Context-Specific Metric Aggregation
We propose a novel weakly supervised discriminative algorithm for learning context specific registration metrics as a linear combination of conventional similarity measures.
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.
Arabidopsis roots segmentation based on morphological operations and CRFs
Arabidopsis thaliana is a plant species widely utilized by scientists to estimate the impact of genetic differences in root morphological features.
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.
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.
Slice-to-volume medical image registration: a survey
During the last decades, the research community of medical imaging has witnessed continuous advances in image registration methods, which pushed the limits of the state-of-the-art and enabled the development of novel medical procedures.
Rigid Slice-To-Volume Medical Image Registration through Markov Random Fields
Rigid slice-to-volume registration is a challenging task, which finds application in medical imaging problems like image fusion for image guided surgeries and motion correction for volume reconstruction.
Prior-based Coregistration and Cosegmentation
We propose a modular and scalable framework for dense coregistration and cosegmentation with two key characteristics: first, we substitute ground truth data with the semantic map output of a classifier; second, we combine this output with population deformable registration to improve both alignment and segmentation.
Sub-cortical brain structure segmentation using F-CNN's
In this paper we propose a deep learning approach for segmenting sub-cortical structures of the human brain in Magnetic Resonance (MR) image data.
