Search Results for author:
Found 34 papers, 10 papers with code
TractOracle: towards an anatomically-informed reward function for RL-based tractography
Reinforcement learning (RL)-based tractography is a competitive alternative to machine learning and classical tractography algorithms due to its high anatomical accuracy obtained without the need for any annotated data.
Fusing Echocardiography Images and Medical Records for Continuous Patient Stratification
Deep learning now enables automatic and robust extraction of cardiac function descriptors from echocardiographic sequences, such as ejection fraction or strain.
Merging multiple input descriptors and supervisors in a deep neural network for tractogram filtering
Their outputs are combined to obtain the classification labels for the streamlines.
Mixup-Privacy: A simple yet effective approach for privacy-preserving segmentation
Our system has two components: 1) a segmentation network on the server side which processes the image mixture, and 2) a segmentation unmixing network which recovers the correct segmentation map from the segmentation mixture.
What Matters in Reinforcement Learning for Tractography
Recently, deep reinforcement learning (RL) has been proposed to learn the tractography procedure and train agents to reconstruct the structure of the white matter without manually curated reference streamlines.
Extraction of volumetric indices from echocardiography: which deep learning solution for clinical use?
Deep learning-based methods have spearheaded the automatic analysis of echocardiographic images, taking advantage of the publication of multiple open access datasets annotated by experts (CAMUS being one of the largest public databases).
FIESTA: Autoencoders for accurate fiber segmentation in tractography
Our proposed method improves bundle segmentation coverage by recovering hard-to-track bundles with generative sampling through the latent space seeding of the subject bundle and the atlas bundle.
ProstAttention-Net: A deep attention model for prostate cancer segmentation by aggressiveness in MRI scans
In this work, we propose a novel end-to-end multi-class network that jointly segments the prostate gland and cancer lesions with GS group grading.
CRISP - Reliable Uncertainty Estimation for Medical Image Segmentation
For this reason, we propose CRISP a ContRastive Image Segmentation for uncertainty Prediction method.
Generative Sampling in Bundle Tractography using Autoencoders (GESTA)
GESTA is thus a novel deep generative bundle tractography method that can be used to improve the tractography reconstruction of the white matter.
Echocardiography Segmentation with Enforced Temporal Consistency
In this paper, we propose a framework to learn the 2D+time apical long-axis cardiac shape such that the segmented sequences can benefit from temporal and anatomical consistency constraints.
Manifold-aware Synthesis of High-resolution Diffusion from Structural Imaging
The physical and clinical constraints surrounding diffusion-weighted imaging (DWI) often limit the spatial resolution of the produced images to voxels up to 8 times larger than those of T1w images.
Deep Learning Based Cardiac MRI Segmentation: Do We Need Experts?
Results reveal that generalization performances of a segmentation neural network trained on non-expert groundtruth data is, to all practical purposes, as good as on expert groundtruth data, in particular when the non-expert gets a decent level of training, highlighting an opportunity for the efficient and cheap creation of annotations for cardiac datasets.
GANs for Medical Image Synthesis: An Empirical Study
The top-performing GANs are capable of generating realistic-looking medical images by FID standards that can fool trained experts in a visual Turing test and comply to some metrics.
Ranked #3 on
Medical Image Generation
on ACDC
Neural Teleportation
In this paper, we explore a process called neural teleportation, a mathematical consequence of applying quiver representation theory to neural networks.
Privacy Preserving for Medical Image Analysis via Non-Linear Deformation Proxy
Results show that the segmentation accuracy of our method is similar to a system trained on non-encoded images, while considerably reducing the ability to recover subject identity.
Filtering in tractography using autoencoders (FINTA)
Current brain white matter fiber tracking techniques show a number of problems, including: generating large proportions of streamlines that do not accurately describe the underlying anatomy; extracting streamlines that are not supported by the underlying diffusion signal; and under-representing some fiber populations, among others.
The Representation Theory of Neural Networks
In this work, we show that neural networks can be represented via the mathematical theory of quiver representations.
Cardiac Segmentation with Strong Anatomical Guarantees
In this paper, we present a framework for producing cardiac image segmentation maps that are guaranteed to respect pre-defined anatomical criteria, while remaining within the inter-expert variability.
On the effectiveness of GAN generated cardiac MRIs for segmentation
On one side of our model is a Variational Autoencoder (VAE) trained to learn the latent representations of cardiac shapes.
LU-Net: a multi-task network to improve the robustness of segmentation of left ventriclular structures by deep learning in 2D echocardiography
Results obtained on a large open access dataset show that our method outperforms the current best performing deep learning solution and achieved an overall segmentation accuracy lower than the intra-observer variability for the epicardial border (i. e. on average a mean absolute error of 1. 5mm and a Hausdorff distance of 5. 1mm) with 11% of outliers.
Prostate Cancer Semantic Segmentation by Gleason Score Group in bi-parametric MRI with Self Attention Model on the Peripheral Zone
Performance of our model was compared to a U-Net baseline model to assess the impact of the self attention module on PCa detection.
Privacy-Net: An Adversarial Approach for Identity-Obfuscated Segmentation of Medical Images
Our novel architecture is composed of three components: 1) an encoder network which removes identity-specific features from input medical images, 2) a discriminator network that attempts to identify the subject from the encoded images, 3) a medical image analysis network which analyzes the content of the encoded images (segmentation in our case).
Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography
Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis.
Cardiac MRI Segmentation with Strong Anatomical Guarantees
In this paper, we propose a cardiac MRI segmentation method which always produces anatomically plausible results.
Spectral Metric for Dataset Complexity Assessment
In this paper, we propose a new measure to gauge the complexity of image classification problems.
Tractography and machine learning: Current state and open challenges
Supervised machine learning (ML) algorithms have recently been proposed as an alternative to traditional tractography methods in order to address some of their weaknesses.
Structured Pruning of Neural Networks with Budget-Aware Regularization
Pruning methods have shown to be effective at reducing the size of deep neural networks while keeping accuracy almost intact.
Non-Local Deep Features for Salient Object Detection
Saliency detection aims to highlight the most relevant objects in an image.
Ranked #2 on
RGB Salient Object Detection
on UCF
GridNet with automatic shape prior registration for automatic MRI cardiac segmentation
In this paper, we propose a fully automatic MRI cardiac segmentation method based on a novel deep convolutional neural network (CNN) designed for the 2017 ACDC MICCAI challenge.
Deep learning trends for focal brain pathology segmentation in MRI
In this chapter, we provide a survey of CNN methods applied to medical imaging with a focus on brain pathology segmentation.
Within-Brain Classification for Brain Tumor Segmentation
Purpose: In this paper, we investigate a framework for interactive brain tumor segmentation which, at its core, treats the problem of interactive brain tumor segmentation as a machine learning problem.
Brain Tumor Segmentation with Deep Neural Networks
Finally, we explore a cascade architecture in which the output of a basic CNN is treated as an additional source of information for a subsequent CNN.
Ranked #1 on
Brain Tumor Segmentation
on BRATS-2013 leaderboard
Retrieval in Long Surveillance Videos using User Described Motion and Object Attributes
We present a content-based retrieval method for long surveillance videos both for wide-area (Airborne) as well as near-field imagery (CCTV).
