Bayesian method transfers knowledge between brain tumor datasets.
problem Applying deep learning to small medical datasets.
method Generative Bayesian Prior network for knowledge transfer.
result Best results in Dice Similarity Coefficient for BRATS2018.
Bayesian method transfers knowledge between brain tumor datasets for MRI segmentation.
problem Limited applicability of deep learning methods for small medical datasets.
method Generative Bayesian Prior network for knowledge transfer.
result Best results in Dice Similarity Coefficient metric for BRATS2018.
A novel 3D U-Net approach improves kidney tumor segmentation in medical imaging.
problem Challenging manual annotation and great medical impact of kidney tumor segmentation.
method End-to-end cascaded U-Nets with a localization network.
result Achieves Sørensen-Dice coefficients of 0.902 for kidney and 0.408 for tumor segmentation.
Bayesian variational inference improves medical image segmentation confidence.
problem Improving interpretability and confidence in deep learning models for medical image segmentation.
method Encoder-decoder architecture based on variational inference for segmenting brain tumor images.
result The model segments brain tumors with both aleatoric and epistemic uncertainty.
Deep learning segments spinal metastases in MR images.
problem Challenges in accurately segmenting spinal metastases in MR images.
method Used a U-Net-like architecture trained on 40 clinical cases.
result Average Dice scores up to 77.6% and mean sensitivity rates up to 78.9%.
Enhanced deep learning model improves tumor segmentation in ultrasound images.
problem Challenges in integrating patient-specific medical priors into deep learning models.
method Integrates visual saliency into a U-Net architecture with attention blocks.
result Achieved a Dice similarity coefficient of 90.5 percent on a dataset of 510 images.
Model infers mineral locations from geospatial data, improving predictions with auxiliary data.
problem Challenges in characterizing hidden mineral deposits underground.
method Generative modeling approach using masked and infilled geospatial maps.
result Models achieve Dice coefficients of 0.31 and recalls of 0.22 at 1×1 mi² resolution.
MI-GAN generates synthetic medical images for supervised analysis.
problem Lack of large datasets and overfitting in medical image analysis.
method Generative Adversarial Network (GAN) for generating synthetic medical images and masks.
result MI-GAN achieves state-of-the-art performance with dice coefficient of 0.837 on STARE and 0.832 on DRIVE datasets.
Deep BV automates brain ventricle segmentation from 3D ultrasound images of mouse embryos.
problem Manual segmentation of brain ventricles from high-frequency ultrasound images is tedious, time-consuming, and requires specialized expertise.
method A fully automated system using two modules: localization and segmentation. Localization identifies a 3D bounding box containing the entire brain ventricle. Segmentation then segments the detected bounding box into brain ventricle and background.
result Achieves a Dice Similarity Coefficient (DSC) of 0.8956 for BV segmentation on an unseen test set, surpassing the previous state-of-the-art method by 25%
Lung segmentation accuracy varies little across diverse datasets.
problem Limited clinical applicability of automated lung segmentation methods.
method Comparison of four deep learning approaches and two standard algorithms on diverse datasets.
result Standard U-net approach yields higher accuracy on routine imaging data.
DICE learns population dynamics from discrete samples.
problem Learning smooth population dynamics from discrete data.
method Discrete Inverse Continuity Equation (DICE) method.
result DICE models are stable and generate representative samples.
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.
Fast, accurate thalamus segmentation method for MS and ET.
problem Automated thalamic nuclei segmentation for neurological diseases.
method Cascaded multi-planar scheme with modified residual U-Net architecture.
result Statistically significant improvements in thalamus segmentation for MS and ET patients.
Paper reduces Hausdorff Distance in medical image segmentation.
problem Reduction of Hausdorff Distance in medical image segmentation.
method Three novel loss functions for training CNNs to estimate and reduce HD.
result Approximately 18-45% reduction in HD without degrading other performance metrics.
DICE estimates data influence cascade in decentralized learning networks.
problem Lack of fair incentives discourages participation in decentralized learning.
method Designs DICE to estimate influence cascade in decentralized networks.
result Influence cascade is influenced by data, topology, and loss landscape curvature.
Few-shot brain segmentation achieved with weak labels and deep networks.
problem Efficient brain segmentation from limited labeled data.
method Heteroscedastic multi-task networks with Monte-Carlo inference and direct probability learning.
result Significant improvements in segmentation accuracy with minimal labeled data.
A new framework for consistent segmentation evaluation reduces operating losses.
problem Inconsistent thresholding-based segmentation methods lead to suboptimal solutions.
method Developed a consistent ranking-based framework (RankDice/RankIoU) using Bayes rules and Dice-/IoU-calibration.
result The proposed framework is Dice-/IoU-calibrated and provides excess risk bounds and convergence rates.
DiCE uses diverse agents to explore and learn, avoiding local minima.
problem Local minima in RL due to limited exploration and correlated behavior.
method DiCE employs a group of heterogeneous agents to explore simultaneously and share experiences, with a diversity regularization mechanism.
result DiCE achieves substantial improvement over baselines in MuJoCo locomotion tasks.
Unified DICE estimators as regularized Lagrangians for improved off-policy evaluation.
problem Improving off-policy evaluation from behavior-agnostic data.
method Unified derivation of DICE estimators as regularized Lagrangians of a linear program.
result Dual solutions offer greater flexibility and provide superior estimates in practice.
Defines an implied CO2-price to cover climate change costs, finding it significantly higher than the SCC.
problem The social cost of carbon (SCC) does not fully cover climate change costs.
method Defines an implied CO2-price as a 'polluter pays principle' and calculates its value using a DICE model.
result The cost-implied CO2 price is around 500/tCO2,comparedto50/tCO2 for SCC. Kidney function evaluation using dynamic contrast-enhanced MRI (DCE-MRI) images could help in diagnosis and treatment of kidney diseases of children. Automatic segmentation of renal parenchyma is an important step in this process. In this paper, we propose a time and memory efficient fully automated segmentation method…
V-Net speeds up brain tumor segmentation in MRI scans.
problem Manual tumor segmentation is time-consuming and inaccurate.
method Applied a volumetric, fully convolutional neural network (V-Net) to MRI scans.
result Achieved a whole tumor dice score of 0.89.
AI tool automates blood segmentation from head CT scans after SAH.
problem Accurate volumetric assessment of SAH patients for clinical and prognostic implications.
method Transformer-based Swin UNETR architecture for noncontrast CT scans.
result High accuracy and robust performance across internal and external validation cohorts.
In this work, we present a comparison of a shallow and a deep learning architecture for the automated segmentation of white matter lesions in MR images of multiple sclerosis patients. In particular, we train and test both methods on early stage disease patients, to verify their performance in challenging conditions, mo…
We present a novel approach to automatically segment magnetic resonance (MR) images of the human brain into anatomical regions. Our methodology is based on a deep artificial neural network that assigns each voxel in an MR image of the brain to its corresponding anatomical region. The inputs of the network capture infor…
U-Det improves lung nodule segmentation in CT images.
problem Challenging shapes and surroundings of lung nodules in CT images.
method End-to-end deep learning with Bi-FPN, Mish activation, and class weights.
result U-Det achieves 82.82% Dice similarity coefficient, comparable to human experts.
This paper adapts PATE for semantic segmentation while maintaining privacy.
problem Preserving privacy in medical machine learning, especially for sensitive information.
method Adapting PATE for semantic segmentation using low-dimensional representations and low-sensitivity queries.
result An Autoencoder-based PATE variant achieves a higher Dice coefficient for the same privacy guarantee.
Background: Cardiac MRI derived biventricular mass and function parameters, such as end-systolic volume (ESV), end-diastolic volume (EDV), ejection fraction (EF), stroke volume (SV), and ventricular mass (VM) are clinically well established. Image segmentation can be challenging and time-consuming, due to the complex a…
Study shows federated learning can segment brain tumors without data sharing.
problem Lack of sufficient medical data for deep learning models.
method Federated learning for multi-institutional collaboration without sharing patient data.
result Federated semantic segmentation models perform similarly to those trained with shared data.
Challenge aims to develop automated meningioma MRI segmentation models.
problem Lack of automated, objective tools for meningioma assessment.
method Develop and evaluate models on largest annotated dataset.
result Improved care of patients with meningioma through automated segmentation.
RankSEG-RMA improves semantic segmentation efficiency and applicability.
problem Inconsistent or suboptimal semantic segmentation results due to argmax or thresholding.
method Developed RankSEG-RMA using reciprocal moment approximation to optimize Dice and IoU metrics.
result RankSEG-RMA reduces computational complexity to O(d) while maintaining comparable performance.
Model shows how discount rates affect intergenerational equity in climate mitigation.
problem Intergenerational equity in climate mitigation decisions.
method Extended DICE model with stochastic discount rates and financing extensions.
result Discount-rate uncertainty amplifies intergenerational inequality in climate mitigation.
Feature extraction is a very crucial task in image and pixel (voxel) classification and regression in biomedical image modeling. In this work we present a machine learning based feature extraction scheme based on inception models for pixel classification tasks. We extract features under multi-scale and multi-layer sche…
White matter hyperintensity (WMH) is commonly found in elder individuals and appears to be associated with brain diseases. U-net is a convolutional network that has been widely used for biomedical image segmentation. Recently, U-net has been successfully applied to WMH segmentation. Random initialization is usally used…
Atrial Fibrillation (AF) is a common electro-physiological cardiac disorder that causes changes in the anatomy of the atria. A better characterization of these changes is desirable for the definition of clinical biomarkers, furthermore, thus there is a need for its fully automatic segmentation from clinical images. In …
3D U-Net improves kidney and tumor segmentation from CT scans.
problem Manual segmentation by clinicians is laborious and error-prone.
method Multi-scale supervised 3D U-Net with deep supervision and post-processing.
result MSS U-Net achieves high Dice coefficients (0.969 for kidney, 0.805 for tumor) on KiTS19 dataset.
QC methods improve reliability of machine learning-based image segmentation.
problem Inaccuracies in machine learning algorithms limit their clinical applicability.
method Analysis and validation of QC approaches for automatic segmentation.
result Aggregation of uncertainty and Dice prediction methods improved segmentation reliability.
The study optimizes supply chain management through a dice-based model to predict cleaner production.
problem Uncertainty in supply chain management and economic predictions.
method A 4-component SC module (environmental, demand, economic, social uncertainties) ranked by weight, using Analytical Hierarchical Process and optimization of a weighted cost function.
result Identifies conditions validating the sustainability of a business venture and optimizes market uncertainty.
Optimization framework for reconstructing missing mandible segments.
problem Missing mandible geometry in jaw reconstructive surgeries.
method Conditional variational autoencoder (CVAE) with weighted multi-target probabilistic solution.
result Statistically significant performance improvement over CVAE.
3D ConvNets improved with Project & Excite for medical imaging segmentation.
problem Improving segmentation performance in 3D medical imaging.
method Proposed Project & Excite (PE) modules for 3D F-CNNs, extending 2D recalibration methods.
result Project & Excite modules boost segmentation performance up to 0.3 in Dice Score.
HD algorithm simulates dynamics on random matrix ensembles without generating full matrices.
problem Simulating dynamics on dense random matrix ensembles with high space and time complexity.
method Householder reflectors for adaptive and recursive construction, deferring decisions.
result Significant reductions in runtime and memory footprint for practical T≪n. Tutorial on interpreting SPECT images for PD recognition using AI.
problem Difficulty in interpreting complex DL models for clinical use.
method Evaluation of six interpretation methods on four DCNN architectures.
result Guided backpropagation and SHAP methods are suitable for PD recognition.
Over half a million individuals are diagnosed with head and neck cancer each year worldwide. Radiotherapy is an important curative treatment for this disease, but it requires manual time consuming delineation of radio-sensitive organs at risk (OARs). This planning process can delay treatment, while also introducing int…
Project aims to diagnose and track IPF disease using deep learning.
problem Diagnosing and tracking IPF disease in lung images.
method Developed a deep learning model for identifying honeycombing and ground glass patterns in HRCT lung images.
result Deep learning model achieved high accuracy in identifying specific lung regions.
Study evaluates different saliency maps for CT image classification.
problem Understanding how deep learning models make decisions in medical imaging.
method Compared several saliency maps using three evaluation metrics.
result Two versions of SmoothGrad performed best across all metrics.
Deep learning models trained on adult cardiac MRI data struggle to accurately segment rare congenital heart diseases.
problem Accuracy of U-Net-based segmentation models trained on adult cardiac MRI data when applied to rare congenital heart diseases like Tetralogy of Fallot.
method Cross-validation with four-fold, evaluation on unseen data from different pathologies.
result Deep learning models overfit to the training data, leading to significant accuracy drops when applied to other pathologies.
Deep learning network matches radiologists in breast cancer segmentation.
problem Automating radiologist-level cancer segmentation from breast MRI.
method 3D U-Net architecture trained on 382,290 breast scans, compared to 255,500 benign cases.
result Network performance matched radiologists' on 2D segmentation of breast cancers.
Entropy corrections improve GBM's predictive accuracy for non-log-normal distributions.
problem Log-normal distribution limitations in GBM predictions.
method Entropy corrections to geometric Brownian motion (GBM).
result Improved predictive accuracy for non-log-normal distributions.