Local semi-supervised method improves brain tissue classification in child MRI.
problem Inaccurate detection of brain tissue classes due to intensity variations in early developing brains.
method Kernel Fisher Discriminant Analysis (KFDA) combined with SSIM for perceptual image quality assessment.
result Optimal brain partitioning into subdomains with different average intensity values and separating surfaces between brain parts.
A neural network learns MRI scan-invariant features for brain tissue classification.
problem Lack of generalization in voxelwise classification methods due to scanner differences.
method Siamese neural network (MRAI-net) to learn acquisition-invariant representations.
result Linear classifier outperforms CNNs on limited training data for tissue classification.
A new method for brain tissue segmentation across medical centers using a smoothness prior.
problem Tissue segmentation challenges due to center-specific acquisition protocols.
method Developed a smoothness prior that is fit to segmentations from another medical center, integrated into an unsupervised Bayesian model.
result Segmentations are similarly smooth across centers, improving generalization.
New deep learning model generates accurate personalized human head models for electromagnetic dosimetry.
problem Challenges in generating accurate human head models for personalized electromagnetic dosimetry.
method Proposed ForkNet architecture for segmentation of whole human head structures using deep learning.
result Generated head models exhibit strong matching with manual segmentation results.
Paper proposes a neural network for estimating brain conductivity without segmentation.
problem Accurate head model generation for personalized TMS with realistic conductivity.
method Convolutional neural network estimating conductivity from MRI data.
result Smooth electric field results similar to conventional methods without segmentation.
Paper presents a brain tumor segmentation method using NGMM and 3D FVF.
problem Automatic brain tumor segmentation from MRIs is challenging.
method Normalized Gaussian Bayesian classifier and 3D Fluid Vector Flow algorithm.
result The method successfully segments brain tumors from MRI images.
Novel approach combines local and global brain changes for AD prediction.
problem Detecting Alzheimer's disease through local and global brain changes.
method Patch-based 3D-CNNs combined with global topological features for multi-scale brain tissue connectivity.
result Average precision score of 0.95 for classifying cognitively normal subjects and AD patients (prevalence ~55%).
A central problem in neuroscience is reconstructing neuronal circuits on the synapse level. Due to a wide range of scales in brain architecture such reconstruction requires imaging that is both high-resolution and high-throughput. Existing electron microscopy (EM) techniques possess required resolution in the lateral p…
Estimates target GGM using auxiliary studies with false discovery rate control.
problem Estimating high-dimensional GGMs from related studies.
method Transfer learning with Trans-CLIME and debiased Trans-CLIME estimators.
result Debiased Trans-CLIME estimator provides element-wise asymptotic normality and false discovery rate control.
New method fills MS lesions more realistically and naturally.
problem Lesions in MS brain scans bias morphometric analyses.
method Non-local partial convolutions (NLPC) integrating Unet-like network and non-local module.
result NLPC generates inpainted regions that appear more realistic and natural.
Deep learning models brain deformations based on atrophy and growth data.
problem Simulating brain deformations due to atrophy and growth.
method Differentiable biomechanical model using deep learning.
result Trained model can rapidly simulate new brain deformations with minimal residuals.
DeepMRSeg uses deep learning for brain tissue segmentation.
problem Automated segmentation of brain tissues and abnormalities.
method Modified UNet architecture with multi-scale feature extraction.
result DeepMRSeg achieves high accuracy on various brain segmentation tasks.
Cellina uses supervised disentanglement to predict cell behavior in tissues.
problem Querying counterfactuals on tissue graphs
method Cellina framework using supervised disentanglement
result Outperforms spatially-informed and non-spatial competitors
Automatically classifying the tissues types of Region of Interest (ROI) in medical imaging has been an important application in Computer-Aided Diagnosis (CAD), such as classification of breast parenchymal tissue in the mammogram, classify lung disease patterns in High-Resolution Computed Tomography (HRCT) etc. Recently…
StackNet predicts fluid intelligence from brain images of adolescents.
problem Predicting fluid intelligence in adolescents using brain imaging.
method Feature extraction, normalization, denoising, selection, StackNet architecture, 11 models, 3 layers, 10-fold cross-validation.
result StackNet achieves mean squared errors of 82.42 on training/validation and 94.25 on testing.
A new deep learning method for tissue-cleared image registration.
problem Efficient registration of high-resolution tissue-cleared images.
method Densely connected convolutional architecture for deformable image registration, unsupervised training.
result Comparable and superior registration performance to state-of-the-art methods, especially at higher resolutions.
A method for MRI brain tumor segmentation using feature vectors and kernel dictionary learning.
problem Segmenting brain tumor regions in MRI images.
method Extracting feature vectors, training kernel dictionaries, and selecting informative feature vectors.
result The method outperforms other methods in segmentation accuracy and reduces training time.
Improved image segmentation across scanners using asymmetric weights.
problem Deteriorated performance of classifiers trained on one scanner for segmentation of images from other scanners.
method A weighted ensemble of classifiers trained on individual images, with weights determined by the similarity between training and test images.
result The bag similarity measure is the most robust and achieves excellent results on various brain and white matter lesion segmentation datasets.
Paper tackles brain tumor segmentation using weak labels and hierarchical training.
problem High manual labeling effort for fully supervised brain tumor segmentation.
method Uses scribbles and global labels for weak supervision, trains two networks in phases.
result Achieves competitive results on brain tumor segmentation and substructure segmentation.
Bayesian layer improves image segmentation and out-of-distribution detection.
problem Outlier detection in image segmentation.
method Parameter-efficient hierarchical convolutional Gaussian Processes in Wasserstein-2 space.
result Uncertainty estimates improve out-of-distribution detection.
Improved MRI head anatomy segmentation using deep learning with multiple priors.
problem Challenges in segmenting head anatomy in MRI, especially with lesions.
method Added three types of prior information to a 3D convolutional network: spatial priors, morphological priors, and spatial context.
result Multiprior network improves segmentation performance, especially for abnormal anatomies.
A CNN integrates with Markov random fields for brain segmentation.
problem Combining CNNs and probabilistic models for robust brain segmentation.
method Backpropagation-based recurrent CNN for spatial interactions in Markov random fields.
result Model generalizes across different imaging protocols.
Predicts cellular functions in human tissues using multi-layer networks.
problem Challenges in predicting tissue-specific cellular function.
method Hierarchy-aware unsupervised node feature learning for multi-layer networks.
result Improves prediction accuracy of cellular functions in 48 tissues.
New deep learning method improves clutter removal in ultrasound imaging.
problem Improving separation of microbubble signal from tissue clutter in ultrasound.
method Robust PCA combined with deep learning for iterative and deep network approaches.
result Deep network architecture outperforms traditional methods in image quality and contrast.
Gene expression levels in a population vary extensively across tissues. Such heterogeneity is caused by genetic variability and environmental factors, and is expected to be linked to disease development. The abundance of experimental data now enables the identification of features of gene expression profiles that are s…
Proposes manifold-based unsupervised anomaly detection for visual data.
problem Rare anomalies in unlabeled data.
method Constant curvature manifolds, hyperspherical Variational Auto-Encoders (VAE) with gyroplane layer.
result State-of-the-art results on visual anomaly benchmarks and histopathology.
This study assesses ML methods for brain tumor segmentation and survival prediction.
problem Segmenting and predicting outcomes of brain tumors with varying sub-regions and heterogeneous properties.
method Evaluation of state-of-the-art machine learning algorithms on BraTS challenge datasets.
result Identification of best ML algorithms for brain tumor segmentation and survival prediction.
Optimizes ASL-MRF scan design for precise brain hemodynamics quantification.
problem Fixing model parameters in ASL introduces bias, and multiparametric estimation degrades precision.
method Optimizes ASL labeling durations using Cramer-Rao Lower Bound (CRLB) and proposes a neural network regression framework.
result Improved precision in estimating multiple hemodynamic parameters from a single scan.
Machine learning classifies colorectal tissue using photoacoustic microscopy.
problem Traditional diagnostic methods for colorectal cancer are limited in detail and painful.
method Machine learning applied to acoustic resolution photoacoustic microscopy.
result Machine learning accurately classified benign and malignant tissue.
PathologyGAN learns deep representations of cancer tissue images.
problem Limited high-quality labels for cancer tissue images.
method Developed a GAN framework for unsupervised learning of cancer tissue phenotypes.
result Generated high-quality images with interpretable latent space.
Study identifies key MRI features for predicting cognitive performance after mTBI.
problem Identify relevant diffusion MRI metrics for cognitive functions in mTBI patients.
method Proposes a novel feature selection method combining best-first search with genetic algorithm crossover.
result Achieves significantly more accurate predictions than other feature selection algorithms.
Model learns cancer tissue images onto a low-dimensional space revealing tissue characteristics.
problem Improving cancer diagnosis through high-fidelity digital pathology.
method Deep generative model using PathologyGAN to map real images onto a latent space.
result Latent space encodes morphological characteristics and reveals distinct tissue clusters.
This paper proposes a new method for estimating sparse precision matrices in the high dimensional setting. It has been popular to study fast computation and adaptive procedures for this problem. We propose a novel approach, called Sparse Column-wise Inverse Operator, to address these two issues. We analyze an adaptive …
VAE improves MSI data analysis for tissue sub-types.
problem Analyzing MSI data from unprocessed samples.
method Applied Variational Autoencoders for data reduction and pattern detection.
result VAEs outperform standard methods in detecting tissue sub-types.
2D tissue model predicts neurotoxicity more accurately and robustly.
problem Fast and accurate prediction of developmental neurotoxicity.
method Machine learning on 2D bio-engineered tissue models.
result 2D model outperforms 3D model in accuracy and robustness.
Universal model for soft tissue mechanics under shock waves.
problem Modeling shock wave mechanics in soft biological tissues.
method Continuum mixture theory with phase-field mechanics.
result Universal thermodynamically consistent formulation for soft porous tissues.
Improved QSM maps from MRI using deep learning.
problem Inaccurate susceptibility maps due to ill-posed dipole inversion.
method 3D GAN with increased receptive field and WGAN with gradient penalty.
result Significantly better QSM maps from single orientation phase maps.
Bayesian Gaussian Processes layer detects out-of-distribution data in medical imaging.
problem Detecting out-of-distribution data in medical imaging tasks.
method Parameter-efficient hierarchical convolutional Gaussian Processes in Wasserstein-2 space.
result Uncertainty estimates enable superior out-of-distribution detection compared to previous methods.
Study uses deep learning to quickly estimate tissue properties for personalized radio-frequency dosimetry.
problem Time-consuming tissue segmentation limits personalized radio-frequency dosimetry.
method Developed a learning-based approach using Convolutional Neural Networks (CNN) for magnetic resonance images.
result Smooth distribution of dielectric properties improves SAR distribution consistency.
Deep learning improves mammography assessment with high accuracy.
problem Challenges in mammography assessment due to noise, resolution, and lack of ground truths.
method Proposes a classification approach using multi-scale deep tissue classifiers.
result Highest AUC of 0.9 achieved in classifying suspicious tissue patches.
A deep learning method automates tensioning for surgical tissue cutting.
problem Automating tensioning for surgical tissue cutting to improve accuracy and reliability.
method Deep reinforcement learning for modeling an autonomous tensioning planner.
result The proposed method outperforms existing methods in terms of performance and robustness.
The study compares different scRNA sequencing methods using a high-dimensional dataset.
problem To identify unique characteristics of different scRNA sequencing methods.
method Quantitative comparison through clustering analysis of a high-dimensional dataset.
result Identifies unique characteristics associated with different scRNA sequencing methods.
We use partial class memberships in soft classification to model uncertain labelling and mixtures of classes. Partial class memberships are not restricted to predictions, but may also occur in reference labels (ground truth, gold standard diagnosis) for training and validation data. Classifier performance is usually ex…
Develops a semi-supervised learning method to generate missing neuroimaging modalities.
problem Lack of paired neuroimaging data for training and inference.
method Semi-Supervised Adversarial CycleGAN (SSA-CGAN) using adversarial and cycle losses.
result Improves reconstruction error and robustness to noise.
MRAI-NET learns MRI scanner-independent features for better tissue segmentation.
problem Invariance of MRI voxelwise classifiers across different scanners.
method Siamese neural network to extract acquisition-invariant feature vectors.
result MRAI-NET outperforms traditional classifiers in small sample settings.
High frequency oscillations (HFOs) are a promising biomarker of epileptic brain tissue and activity. HFOs additionally serve as a prototypical example of challenges in the analysis of discrete events in high-temporal resolution, intracranial EEG data. Two primary challenges are 1) dimensionality reduction, and 2) asses…
New strategies help CNNs recognize tissue features across different stains.
problem Training deep learning models for images with multiple stains is challenging and expensive.
method Presented unsupervised training strategies that leverage one staining modality to improve performance on images with multiple stains.
result CNNs trained with these strategies outperform standard training methods on images with multiple stains.
Deep neural networks infer multiple cancer properties from transcriptome data.
problem Limited use of biomarkers in molecular cancer pathology due to computational challenges.
method Multi-task and transfer learning architecture encoding whole transcriptome into a latent vector.
result Significantly better at predicting tissue-of-origin, disease state, and cancer type.