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.
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48 results for “Dice loss”
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 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.
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 …
Proposes a new network to improve nuclei segmentation in histopathology images.
problem Challenges in separating overlapped nuclei in histopathology images.
method Introduces a bending loss regularized network to minimize contour points with large curvatures.
result Outperforms six state-of-the-art approaches on five quantitative metrics.
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.
Few-shot brain segmentation from weakly labeled data with deep heteroscedastic multi-task networkscs.LG
In applications of supervised learning applied to medical image segmentation, the need for large amounts of labeled data typically goes unquestioned. In particular, in the case of brain anatomy segmentation, hundreds or thousands of weakly-labeled volumes are often used as training data. In this paper, we first observe…
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.
Brain cancer can be very fatal, but chances of survival increase through early detection and treatment. Doctors use Magnetic Resonance Imaging (MRI) to detect and locate tumors in the brain, and very carefully analyze scans to segment brain tumors. Manual segmentation is time consuming and tiring for doctors, and it ca…
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.
Empirical risk minimization frequently employs convex surrogates to underlying discrete loss functions in order to achieve computational tractability during optimization. However, classical convex surrogates can only tightly bound modular loss functions, sub-modular functions or supermodular functions separately while …
Topology-enhanced loss improves 3D object reconstruction from 2D images.
problem Challenges in reconstructing 3D objects from 2D images, especially capturing shape information.
method Integrates multi-scale topological features into the reconstruction loss using cubical complexes and optimal transport distance.
result Topology-aware loss substantially improves 3D reconstruction quality.
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 for SCC.
Reducing the Hausdorff Distance in Medical Image Segmentation with Convolutional Neural Networkseess.IV
The Hausdorff Distance (HD) is widely used in evaluating medical image segmentation methods. However, existing segmentation methods do not attempt to reduce HD directly. In this paper, we present novel loss functions for training convolutional neural network (CNN)-based segmentation methods with the goal of reducing HD…
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.
Boosting theory extended to handle cost-sensitive and multi-objective losses.
problem Real-world prediction problems with different error costs.
method Developed a comprehensive theory of cost-sensitive and multi-objective boosting.
result Established a dichotomy for binary classification and a more intricate landscape for multiclass settings.
Automated Thalamic Nuclei Segmentation Using Multi-Planar Cascaded Convolutional Neural Networkseess.IV
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.
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%.
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.
Deep learning models for semantic segmentation of images require large amounts of data. In the medical imaging domain, acquiring sufficient data is a significant challenge. Labeling medical image data requires expert knowledge. Collaboration between institutions could address this challenge, but sharing medical data to…
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.
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.
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.
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…
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.
The paper uses neural networks to segment brain tumors and predict patient survival.
problem Brain tumor segmentation and survival prediction.
method Fully convolutional neural network with encoder-decoder architecture, radiomic features, and random forest regression.
result 55.4% classification accuracy for predicting survival of patients with gross-total resection.
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.
Cleaner Production in Optimized Multivariate Networks: Operations Management through a Roll of Diceq-fin.GN
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.
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.
The clinical management of several cardiovascular conditions, such as pulmonary hypertension, require the assessment of the right ventricular (RV) function. This work addresses the fully automatic and robust access to one of the key RV biomarkers, its ejection fraction, from the gold standard imaging modality, MRI. The…
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.
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 .
Proposes a method to improve weakly supervised image segmentation using deep geodesic priors.
problem Limited availability of high-quality annotations for image segmentation, especially in medical data.
method Integrates a deep geodesic prior extracted from an auto-encoder to reduce the adverse effects of weak labels in segmentation accuracy.
result The proposed method significantly improves segmentation accuracy, boosting performance by 4.4% in dice score for clean labels and up to 6.3% for noisy labels (L2).
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.
This paper uses CNNs to automatically segment ischaemic stroke lesions from MRI sequences.
problem Automatically segmenting ischaemic stroke lesions from MRI sequences is challenging.
method Adversarial training of CNNs on multi-sequence MRI data.
result The method achieves high Dice scores for core and penumbra segmentation.
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.
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.
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.
Radiologist-level Performance by Using Deep Learning for Segmentation of Breast Cancers on MRI Scanscs.LG
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 corrected geometric Brownian motionphysics.data-an
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.
Stochasticity is key for machine learning's robustness and generalizability.
problem Machine learning's need for robustness and generalizability.
method Review of ML literature and biological intelligence.
result Stochasticity is a critical ingredient for intelligent systems in ML.
In this work we propose a method for anatomical data augmentation that is based on using slices of computed tomography (CT) examinations that are adjacent to labeled slices as another resource of labeled data for training the network. The extended labeled data is used to train a U-net network for a pixel-wise classific…
Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multi-Sequence MRIeess.IV
Detecting and segmenting brain metastases is a tedious and time-consuming task for many radiologists, particularly with the growing use of multi-sequence 3D imaging. This study demonstrates automated detection and segmentation of brain metastases on multi-sequence MRI using a deep learning approach based on a fully con…
Study improves semantic segmentation of LiDAR point clouds for autonomous vehicles.
problem Semantic understanding of 3D LiDAR data for autonomous vehicles.
method Image-based semantic segmentation architectures, network size optimization, improved point cloud projection, soft Dice loss, reduced weight-sharing convolution.
result 3.2% increase in mIoU segmentation performance with 42% reduction in inference time.
Modeling climate change costs with stochastic interest rates shows inequality, but funding abatement can reduce this.
problem Evaluating the costs and benefits of climate change mitigation with uncertain discount rates.
method Amended DICE model with stochastic interest rates and funding abatement costs.
result Introducing funding abatement can reduce intergenerational inequality in climate change costs.