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.
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.
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…
Deep learning improves tumor type classification accuracy.
problem Classifying cancer types based on DNA mutations is challenging.
method Deep transfer learning and fine-tuning of gene expression data.
result Significantly improved tumor type classification accuracy (78.3%) using DNA point mutations.
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.
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.
Paper proposes scalable method for analyzing multi-omic data.
problem Integrating high-dimensional multi-omic data for cancer subtyping.
method Mixed graphical model approach using Birth-Death MCMC algorithm.
result Our method outperforms LASSO and standard BDMCMC in computational efficiency and model selection accuracy.
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…
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.
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
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.
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.
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.
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.
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.
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.
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.
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.
Novel method embeds generative model into Bayesian optimization for HD cardiac model parameter estimation.
problem High-dimensional optimization of patient-specific cardiac model parameters with limited data.
method Embeds a generative variational auto-encoder into the objective function of Bayesian optimization.
result Improves accuracy of parameter estimation with more than 10x gain in efficiency.
Cartesian neural network models learn soft tissue mechanical properties without shape assumptions.
problem Model-based methods limit elastography to imaging linear-elastic parameters.
method Data-driven neural network constitutive models (NNCMs) learn stress-strain relationships from force-displacement data.
result NNCMs can characterize mechanical properties and their spatial distribution without prior shape knowledge.
Deep learning predicts tissue properties from cell-laden hydrogels.
problem Predicting tissue properties from cell-laden hydrogels.
method Developed a process for generating mould designs, created a training set of 6500 cases, trained a deep learning model (pix2pix).
result Deep learning makes excellent predictions and is significantly faster than biophysical methods.
Deep network improves electrical tomography across multiple frequencies.
problem Nonlinear multi-frequency electrical impedance tomography (mfEIT) for tissue conductivity estimation.
method Integrates graph neural networks (GNNs) into the iterative Proximal Regularized Gauss Newton (PRGN) framework to reconstruct tissue concentrations accurately.
result Accurate reconstruction of overlapping tissue fraction concentrations across multiple frequencies.
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.
The curvature of web curves is studied in 3D manifolds.
problem Understanding the maximal rank of web structures in 3D.
method Defined a tautological connection on a bundle whose curvature vanishes for maximal rank.
result Exceptional 6-web and its subwebs in 3D have maximal rank.
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.
Physics-Informed Neural Networks (PINNs) benchmarked against clinical estimator and reveal parameter identifiability
problem Chemotherapy pharmacokinetics (PK) with tissue concentration not measured
method Physics-Informed Neural Networks (PINNs) benchmarked against clinical estimator and reveal parameter identifiability
result PINN recovers tissue concentration and identifies non-identifiable parameters
Bayesian optimization on cardiac models using a graph convolutional VAE.
problem Optimizing tissue properties in cardiac models with spatially varying properties.
method Graph convolutional VAE for generative modeling of non-Euclidean data.
result Effective optimization of cardiac tissue properties using a novel generative model.
EAGLE-Net enhances foundation models by integrating patch-level features for better tissue understanding.
problem Foundation models lack mechanisms for global tissue structure and local context in computational pathology.
method EAGLE-Net combines multi-scale spatial encoding, attention-guided loss functions, and background suppression to aggregate patch-level features into slide-level predictions.
result EAGLE-Net improves classification accuracy and concordance indices across multiple cancer types, producing biologically coherent attention maps.
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.
Theory models nonlinear soft tissue elasticity and remodeling using extended Finsler geometry.
problem Understanding and predicting the behavior of nonlinear soft tissues, especially in biologic contexts.
method Formulated a continuum mechanical theory incorporating extended Finsler geometry to describe the complex behaviors of fibrous soft solids.
result The model quantifies residual strains from growth, remodeling, and degradation, and predicts equilibrium configurations.
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.
We release a large ECG dataset for arrhythmia subtype discovery.
problem Discovering unknown subtypes of arrhythmia from continuous raw signals.
method Unsupervised representation learning task using semi-supervised evaluation.
result Qualitative evaluations show potential for representation learning in arrhythmia sub-type discovery.
Over the last years, huge resources of biological and medical data have become available for research. This data offers great chances for machine learning applications in health care, e.g. for precision medicine, but is also challenging to analyze. Typical challenges include a large number of possibly correlated featur…
CNN improves frame selection for ultrasound elastography.
problem Choosing suitable frames for accurate strain estimation in ultrasound elastography.
method Convolutional Neural Network (CNN) for frame selection.
result CNN selects frames in 5.4 ms for high-quality strain images.
TACOMA improves cancer biomarker validation by incorporating deep features.
problem Improving accuracy and repeatability in TMA image scoring.
method Incorporating deep learning representations learned through unsupervised clustering and recursive space partitioning.
result Reduced error rate by about 6% on breast cancer TMA images.
Deep learning system classifies breast cancer biopsy images with high accuracy.
problem Accurately differentiate between normal and cancerous breast tissue.
method Convolutional capsule network for four types of breast biopsy images.
result Cross-validation accuracy of 0.87 with high sensitivity.
New biomarker predicts MRgFUS treatment outcome without contrast agents.
problem Inaccurate assessment of treated tissue viability after MRgFUS.
method Deep learning on noncontrast multiparametric MRI images, voxel-wise registration.
result Predicted follow-up NPV with DICE coefficient 0.71, outperforming current standard.
AI enhances cancer diagnostics using spectroscopy.
problem Early and accurate cancer diagnosis.
method Combining AI with spectroscopy-based techniques.
result AI improves cancer diagnostics speed and safety.
We give a complete proof of the fact that the trace of the curvature of the connection associated to a planar d-web (d>3) is the sum of the Blaschke curvatures of its sub 3-webs.
Tissue heterogeneity is a major confounding factor in studying individual populations that cannot be resolved directly by global profiling. Experimental solutions to mitigate tissue heterogeneity are expensive, time consuming, inapplicable to existing data, and may alter the original gene expression patterns. Here we a…
Deep learning speeds sound speed inversion in ultrasound.
problem Limited high-end ultrasound hardware for shear wave imaging.
method Fully convolutional deep neural network using simulated data.
result Inversion of longitudinal sound speed at high frame rates.
Generative models create H&E-stained and destained prostate biopsy images.
problem Lack of H&E-stained prostate biopsy images.
method Conditional GAN for H&E staining, destaining model learning from stained to non-stained images.
result Generated images maintain structural similarity to non-stained biopsy.
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%).
Novel framework assesses optical imaging hardware uncertainties.
problem Uncertainty in optical imaging modalities, especially ambiguity in parameter estimation.
method Invertible neural networks to map multispectral measurements to posterior probability distributions.
result Ambiguity in blood volume fraction estimation is a key finding.