Search Results for author:
Found 47 papers, 10 papers with code
Label Refinement Network from Synthetic Error Augmentation for Medical Image Segmentation
In this paper, we propose a novel label refinement method to correct such errors from an initial segmentation, implicitly incorporating information about label structure.
Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021
This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels.
A Quantitative Comparison of Epistemic Uncertainty Maps Applied to Multi-Class Segmentation
This paper highlights a systematic approach to define and quantitatively compare those methods in two different contexts: class-specific epistemic uncertainty maps (one value per image, voxel and class) and combined epistemic uncertainty maps (one value per image and voxel).
Deep Learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge
The publicly available database consists of 150 exams divided into 50 cases with normal MRI after injection of a contrast agent and 100 cases with myocardial infarction (and then with a hyperenhanced area on DE-MRI), whatever their inclusion in the cardiac emergency department.
Automated Segmentation and Volume Measurement of Intracranial Carotid Artery Calcification on Non-Contrast CT
To evaluate the method, we compared manual and automatic assessment (computed using ten-fold cross-validation) with respect to 1) the agreement with an independent observer's assessment (available in a random subset of 47 scans); 2) the accuracy in delineating ICAC as judged via blinded visual comparison by an expert; 3) the association with first stroke incidence from the scan date until 2012.
Adversarial Heart Attack: Neural Networks Fooled to Segment Heart Symbols in Chest X-Ray Images
Previous studies have shown that it is possible to adversarially manipulate automated segmentations produced by neural networks in a targeted manner in the white-box attack setting.
Crowdsourcing Airway Annotations in Chest Computed Tomography Images
We generate image slices at known locations of airways in 24 subjects and request the crowd workers to outline the airway lumen and airway wall.
Medical Imaging with Deep Learning: MIDL 2020 -- Short Paper Track
This compendium gathers all the accepted extended abstracts from the Third International Conference on Medical Imaging with Deep Learning (MIDL 2020), held in Montreal, Canada, 6-9 July 2020.
Region-of-interest guided Supervoxel Inpainting for Self-supervision
Self-supervised learning has proven to be invaluable in making best use of all of the available data in biomedical image segmentation.
DS6, Deformation-aware Semi-supervised Learning: Application to Small Vessel Segmentation with Noisy Training Data
The deep learning model based on U-Net Multi-Scale Supervision was trained using the training subset and was made equivariant to elastic deformations in a self-supervised manner using deformation-aware learning to improve the generalisation performance.
Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored Factors
Firstly, we study the effect of weight initialization (ImageNet vs. random) on the transferability of adversarial attacks from the surrogate model to the target model.
Spectral Data Augmentation Techniques to quantify Lung Pathology from CT-images
Data augmentation is of paramount importance in biomedical image processing tasks, characterized by inadequate amounts of labelled data, to best use all of the data that is present.
When Weak Becomes Strong: Robust Quantification of White Matter Hyperintensities in Brain MRI scans
We showed that networks optimized using only weak labels reflecting WMH volume generalized better for WMH volume prediction than networks optimized with voxel-wise segmentations of WMH.
Semi-Supervised Medical Image Segmentation via Learning Consistency under Transformations
In this paper, we propose a novel semi-supervised method that, in addition to supervised learning on labeled training images, learns to predict segmentations consistent under a given class of transformations on both labeled and unlabeled images.
Automated Estimation of the Spinal Curvature via Spine Centerline Extraction with Ensembles of Cascaded Neural Networks
For diagnosis and treatment planning of scoliosis, spinal curvature can be estimated using Cobb angles.
APIR-Net: Autocalibrated Parallel Imaging Reconstruction using a Neural Network
Deep learning has been successfully demonstrated in MRI reconstruction of accelerated acquisitions.
A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs
In this approach, the convolutional layers at the deepest level of the UNet are replaced by a GNN-based module with a series of graph convolutions.
Automated Lesion Detection by Regressing Intensity-Based Distance with a Neural Network
We show the potential of this approach to detect enlarged perivascular spaces in white matter on a large brain MRI dataset with an independent test set of 1000 scans.
Multi-Task Attention-Based Semi-Supervised Learning for Medical Image Segmentation
The reconstruction objective uses an attention mechanism that separates the reconstruction of image areas corresponding to different classes.
Multi-atlas image registration of clinical data with automated quality assessment using ventricle segmentation
In this work, we propose to automatically assess the quality of registration to an atlas in clinical FLAIR MRI scans of the brain.
Weakly Supervised Object Detection with 2D and 3D Regression Neural Networks
For comparison, we modify state-of-the-art methods to compute attention maps for weakly supervised object detection, by using a global regression objective instead of the more conventional classification objective.
A cross-center smoothness prior for variational Bayesian brain tissue segmentation
Here we present a smoothness prior that is fit to segmentations produced at another medical center.
Event-Based Modeling with High-Dimensional Imaging Biomarkers for Estimating Spatial Progression of Dementia
In order to validate the biomarker ordering obtained using nDEBM, we also present a framework for Simulation of Imaging Biomarkers' Temporal Evolution (SImBioTE) that mimics neurodegeneration in brain regions.
Graph Refinement based Airway Extraction using Mean-Field Networks and Graph Neural Networks
Graph refinement, or the task of obtaining subgraphs of interest from over-complete graphs, can have many varied applications.
An End-to-end Approach to Semantic Segmentation with 3D CNN and Posterior-CRF in Medical Images
Fully-connected Conditional Random Field (CRF) is often used as post-processing to refine voxel classification results by encouraging spatial coherence.
Learning to quantify emphysema extent: What labels do we need?
We evaluate performance on 600 low-dose CT scans from the Danish Lung Cancer Screening Trial and find that learning from emphysema presence labels, which are much easier to obtain, gives equally good performance to learning from emphysema extent labels.
Deep Learning from Label Proportions for Emphysema Quantification
We propose an end-to-end deep learning method that learns to estimate emphysema extent from proportions of the diseased tissue.
Hydranet: Data Augmentation for Regression Neural Networks
The proposed method reached an intraclass correlation coefficient between ground truth and network predictions of 0. 73 on the first task and 0. 84 on the second task, only using between 25 and 30 scans with a single global label per scan for training.
Extracting Tree-structures in CT data by Tracking Multiple Statistically Ranked Hypotheses
We propose to use statistical ranking of local hypotheses in constructing the MHT tree, which yields a probabilistic interpretation of scores across scales and helps alleviate the scale-dependence of MHT parameters.
Feature learning based on visual similarity triplets in medical image analysis: A case study of emphysema in chest CT scans
We evaluate the networks on 973 images, and show that the CNNs can learn disease relevant feature representations from derived similarity triplets.
Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
Machine learning (ML) algorithms have made a tremendous impact in the field of medical imaging.
Extraction of Airways using Graph Neural Networks
We present extraction of tree structures, such as airways, from image data as a graph refinement task.
Mean Field Network based Graph Refinement with application to Airway Tree Extraction
Performance of the method is compared with two methods: the first uses probability images from a trained voxel classifier with region growing, which is similar to one of the best performing methods at EXACT'09 airway challenge, and the second method is based on Bayesian smoothing on these probability images.
Quantification of Lung Abnormalities in Cystic Fibrosis using Deep Networks
We compare our method with random forest and a single neural network approach.
3D Regression Neural Network for the Quantification of Enlarged Perivascular Spaces in Brain MRI
We propose a convolutional network regression method to quantify the extent of EPVS in the basal ganglia from 3D brain MRI.
Extraction of Airways with Probabilistic State-space Models and Bayesian Smoothing
The evolution of individual branches is modelled using a process model and the observed data is incorporated into the update step of the Bayesian smoother using a measurement model that is based on a multi-scale blob detector.
Automatic Emphysema Detection using Weakly Labeled HRCT Lung Images
The classifier has a stronger correlation with PFT than the density based method, the percentage of emphysema in the intersection of annotations from both radiologists, and the percentage of emphysema annotated by one of the radiologists.
Early Experiences with Crowdsourcing Airway Annotations in Chest CT
Measuring airways in chest computed tomography (CT) images is important for characterizing diseases such as cystic fibrosis, yet very time-consuming to perform manually.
Segmentation of Intracranial Arterial Calcification with Deeply Supervised Residual Dropout Networks
In this paper, we propose a method for automatic segmentation of ICAC; the first to our knowledge.
GP-Unet: Lesion Detection from Weak Labels with a 3D Regression Network
We train a regression network with a fully convolutional architecture combined with a global pooling layer to aggregate the 3D output into a scalar indicating the lesion count.
Classification of COPD with Multiple Instance Learning
Chronic obstructive pulmonary disease (COPD) is a lung disease where early detection benefits the survival rate.
Transfer Learning by Asymmetric Image Weighting for Segmentation across Scanners
We show that the asymmetry can indeed be informative, and that computing the similarity from the test image to the training images is more appropriate than the opposite direction.
Label Stability in Multiple Instance Learning
We address the problem of \emph{instance label stability} in multiple instance learning (MIL) classifiers.
Transfer learning for multi-center classification of chronic obstructive pulmonary disease
We show that Gaussian texture features outperform intensity features previously used in multi-center classification tasks.
Extraction of airway trees using multiple hypothesis tracking and template matching
The results show improvements in performance when compared to the original method and region growing on intensity images.
Scalable kernels for graphs with continuous attributes
While graphs with continuous node attributes arise in many applications, state-of-the-art graph kernels for comparing continuous-attributed graphs suffer from a high runtime complexity; for instance, the popular shortest path kernel scales as $\mathcal{O}(n^4)$, where $n$ is the number of nodes.
Geometric tree kernels: Classification of COPD from airway tree geometry
Methodological contributions: This paper introduces a family of kernels for analyzing (anatomical) trees endowed with vector valued measurements made along the tree.
