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Found 20 papers, 11 papers with code
No-Clean-Reference Image Super-Resolution: Application to Electron Microscopy
The inability to acquire clean high-resolution (HR) electron microscopy (EM) images over a large brain tissue volume hampers many neuroscience studies.
Optimizing Feature Selection for Binary Classification with Noisy Labels: A Genetic Algorithm Approach
Feature selection in noisy label scenarios remains an understudied topic.
Convolutional Neural Networks for Automatic Detection of Intact Adenovirus from TEM Imaging with Debris, Broken and Artefacts Particles
To overcome the challenge, due to such a presence, we developed a software tool for semi-automatic annotation and segmentation of adenoviruses and a software tool for automatic segmentation and detection of intact adenoviruses in TEM imaging systems.
Self-Supervised Super-Resolution Approach for Isotropic Reconstruction of 3D Electron Microscopy Images from Anisotropic Acquisition
To overcome this limitation, we propose a novel deep-learning (DL)-based self-supervised super-resolution approach that computationally reconstructs isotropic 3DEM from the anisotropic acquisition.
Multi-Objective Genetic Algorithm for Multi-View Feature Selection
However, the use of multi-view data leads to an increase in high-dimensional data, which poses significant challenges for the prediction models that can lead to poor generalization.
Sauron U-Net: Simple automated redundancy elimination in medical image segmentation via filter pruning
We present Sauron, a filter pruning method that eliminates redundant feature maps by discarding the corresponding filters with automatically-adjusted layer-specific thresholds.
Adversarial Distortion Learning for Medical Image Denoising
The proposed ADL consists of two auto-encoders: a denoiser and a discriminator.
Ranked #1 on
Color Image Denoising
on CBSD68 sigma35
Multi-task longitudinal forecasting with missing values on Alzheimer's Disease
Machine learning techniques typically applied to dementia forecasting lack in their capabilities to jointly learn several tasks, handle time dependent heterogeneous data and missing values.
gACSON software for automated segmentation and morphology analyses of myelinated axons in 3D electron microscopy
In this work, we introduce a freely available Matlab-based gACSON software for visualization, segmentation, assessment, and morphology analysis of myelinated axons in 3D-EM volumes of brain tissue samples.
Comparison of single and multitask learning for predicting cognitive decline based on MRI data
The Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) is a neuropsychological tool that has been designed to assess the severity of cognitive symptoms of dementia.
Automatic cerebral hemisphere segmentation in rat MRI with lesions via attention-based convolutional neural networks
We present MedicDeepLabv3+, a convolutional neural network that is the first completely automatic method to segment cerebral hemispheres in magnetic resonance (MR) volumes of rats with lesions.
Region-wise Loss for Biomedical Image Segmentation
We show that, under the proposed RW loss framework, certain loss functions, such as Active Contour and Boundary loss, can be reformulated similarly with appropriate RW maps, thus revealing their underlying similarities and a new perspective to understand these loss functions.
Transfer Learning in Magnetic Resonance Brain Imaging: a Systematic Review
We found 129 articles that applied transfer learning to brain MRI tasks.
Evaluation of machine learning algorithms for Health and Wellness applications: a tutorial
Research on decision support applications in healthcare, such as those related to diagnosis, prediction, treatment planning, etc., have seen enormously increased interest recently.
The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up
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.
RatLesNetv2: A Fully Convolutional Network for Rodent Brain Lesion Segmentation
RatLesNetv2 obtained similar to higher Dice coefficient values than the other ConvNets and it produced much more realistic and compact segmentations with notably fewer holes and lower Hausdorff distance.
Cylindrical Shape Decomposition for 3D Segmentation of Tubular Objects
We develop a cylindrical shape decomposition (CSD) algorithm to decompose an object, a union of several tubular structures, into its semantic components.
Predicting intelligence based on cortical WM/GM contrast, cortical thickness and volumetry
These last two measurements were derived from the T1-weighted MR images using cortical surfaces produced by the CIVET pipeline.
Bayesian Receiver Operating Characteristic Metric for Linear Classifiers
We derive a closed-form solution of the proposed accuracy metric for any linear binary classifier under the Gaussianity assumption, and study the accuracy of the proposed estimator using simulated and real-world data.
Automatic Rodent Brain MRI Lesion Segmentation with Fully Convolutional Networks
Several automatic methods have been developed for different human brain MRI segmentation, but little research has targeted automatic rodent lesion segmentation.
