Transfer learning from breast histopathology improves prostate cancer detection.
problem Insufficient prostate histopathology datasets for training.
method Transfer learning from breast histopathology images.
result Proposed approach outperforms transfer learning from ImageNet dataset.
Deep CNNs segment prostate from MRI images.
problem Variability and scarcity of annotated prostate MRI data.
method Two-step CNN approach: global bounding box, local delineation; data augmentation.
result State-of-the-art Dice scores (90.6) on PROMISE12 dataset.
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.
Proposes using MR images to create synthetic CT images for prostate segmentation.
problem Creating high-quality annotations for prostate segmentation in CT scans.
method CycleGAN algorithm to create synthetic CT images from MR images, using a 2.5D Residual U-Net for segmentation.
result Automatic delineation of prostate from real CT scans achieved with comparable results to radiologist annotations.
Method registers prostate scans from different times and equipment.
problem Registration of volumetric prostate scans from different times and equipment.
method Curvature flow for mapping and alignment of prostate datasets.
result Method produces homeomorphisms with feature constraints.
CNN-based prostate cancer grading improves accuracy and efficiency.
problem Manual Gleason grading by pathologists is time-consuming and prone to errors.
method Patch-Based Image Reconstruction (PBIR), Distribution Correction (DC), Quadratic Weighted Mean Square Error (QWMSE).
result Achieved superior expert-level performance (0.8885 quadratic-weighted kappa coefficient).
Deep neural networks predict prostate motion from MR images.
problem Predicting prostate motion during ultrasound-guided interventions.
method Biomechanically-trained deep neural networks on unstructured nodes.
result Trained networks yield near real-time inference with 0.017 mm error.
Deep learning improves prostate cancer diagnosis using MRI.
problem Improving accuracy in prostate cancer diagnosis.
method Developed XmasNet, a deep learning architecture based on convolutional neural networks, trained on 3D multiparametric MRI data.
result XmasNet outperformed traditional machine learning models and achieved the second highest AUC (0.84) in the PROSTATEx challenge.
Study evaluates data augmentation methods for prostate cancer detection in MRI.
problem Limited data for prostate cancer detection in MRI.
method Static application of five augmentation techniques (rotation, flip, crop, translation) to MRI dataset.
result Rotation method improved 2D slice-based AUC to 0.85.
Spatially-aware metrics improve uncertainty evaluation in segmentation.
problem Uncertainty evaluation metrics treat voxels independently, ignoring spatial context.
method Proposed three spatially aware metrics incorporating structural and boundary information.
result Improved alignment with clinically important factors and better discrimination between uncertainty patterns.
Adversarial learning improves image registration networks without smoothness penalties.
problem Training image registration networks with weak labels and without smoothness constraints.
method Adversarial learning to regularize network predictions, using biomechanical simulations.
result End-to-end trained network predicts plausible deformations with minimal smoothness penalties.
This paper synthesizes mp-MRI data for prostate cancer detection.
problem Synthesizing clinically significant prostate cancer MRI data.
method Semi-supervised adversarial learning with StitchLayer and auxiliary distance maximization.
result Synthesizes high-quality mp-MRI images with distinguishable prostate cancer lesions.
Deep learning and active learning improve survival analysis for prostate cancer treatment recommendations.
problem Building accurate survival models from high-dimensional, sparse, and complex clinical data.
method Deep active survival analysis framework using labeled and unlabeled instances, active learning with oracle.
result Our approach outperforms baseline models in prostate cancer treatment recommendation.
Proposes a multi-resolution model for prostate cancer classification using mpMRI.
problem Improving voxel-wise classification of prostate cancer using multi-parametric MRI data.
method Multi-resolution Super Learner framework combining local base learners at multiple resolutions and spatial Gaussian kernel smoothing.
result Enhanced voxel-wise classification of prostate cancer status and clinical significance.
A tool simplifies neural network training for medical image analysis.
problem Difficulties in training neural networks for medical image analysis.
method Intuitive interface for WSI annotation and display, human-in-the-loop strategy.
result Improved network performance through iterative annotation.
Enhances uncertainty estimation in medical image segmentation.
problem Frequency-related noise in medical imaging leads to biased uncertainty estimates.
method Extends MC-Dropout to the frequency domain for better uncertainty estimation.
result MC-Frequency Dropout improves calibration and uncertainty in semantic segmentation.
Study builds tools to detect misinformation in online medical videos.
problem Misleading information in online medical videos.
method Manual annotation of a dataset, use of linguistic, acoustic, and user engagement features for classification models.
result Automatic models can identify misinformation with up to 74% accuracy.
New method for causal inference in survival outcomes using RDD.
problem Censoring in time-to-event analyses.
method Nonparametric approach with doubly robust censoring corrections.
result Higher efficiency and robustness to misspecification.
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.
Adapts CNN for robust medical image segmentation across different scanners and protocols.
problem Performance degradation of CNNs in medical image segmentation due to mismatch between training and test images.
method Designs a segmentation CNN as a concatenation of a shallow normalization CNN and a deep CNN. At test time, adapts the normalization sub-network for each test image using a denoising autoencoder.
result Consistently improves performance on multi-center MRI datasets of brain, heart, and prostate.
Study compares DL models for medical image segmentation, finds synergistic ensemble strategies improve performance.
problem Improving DL models for specialized medical image segmentation using transfer learning.
method Detailed comparisons of TII and LMI models for binary segmentation of medical images.
result Ensemble strategies improve performance by 10% in certain scenarios.
Deep neural networks improve cancer nuclei classification and survival analysis.
problem Accurate cell nuclei classification for cancer diagnosis and survival analysis.
method Implementation and evaluation of deep neural network models and ensembles for nuclei classification in RCC and PCa.
result Convolutional neural network system based on residual learning significantly improves cell nuclei classification.
Spectral decoupling improves neural network generalization in medical imaging.
problem Poor generalization of neural networks trained on medical imaging data.
method Spectral decoupling, a regularization technique that encourages learning more features.
result Spectral decoupling increases network robustness and performance on external datasets.
Improves uncertainty estimates in lung node segmentation.
problem Inaccurate uncertainty estimates in medical segmentation.
method Supervised learning using multi-grader annotation variability.
result Improves predictive uncertainty estimates and sample diversity.
Proposes a new contrastive loss for semi-supervised medical image segmentation.
problem Lack of labeled data for medical image segmentation.
method Uses pseudo-labels and a local contrastive loss to learn good local representations.
result Achieved high segmentation performance on public cardiac and prostate datasets.
Develops platforms to analyze social media data for human behavior and emotions.
problem Understanding human behavior and emotions from social media data.
method Self-structuring incremental machine learning, event detection, natural language processing.
result Captured salient topics and events from social media data, validated against news.
Optimal cutoff interval for risk scores improves binary classification accuracy.
problem Improving binary classification accuracy with abstention.
method Determines optimal cutoff interval for risk scores, refraining from decisions outside this interval.
result Minimizes classification margin and maximizes accuracy within the interval.
VarDeepPCA refines medical image segmentation from small datasets, improving anatomical plausibility and reducing errors.
problem Medical image segmentation fails on out-of-distribution data due to variations in scanners and protocols.
method VarDeepPCA learns valid anatomical geometries using only small in-distribution datasets, providing uncertainty estimates.
result VarDeepPCA restores segmentation maps to OOD data, improving anatomical plausibility and reducing errors.
The paper develops a method to learn cost-optimal sequential testing policies from retrospective data.
problem Learning cost-optimal sequential decision policies from retrospective data with missing test results.
method Doubly robust Q-learning framework with path-specific inverse probability weights.
result The method reduces testing cost without compromising predictive accuracy.
Bayesian model predicts sexual function recovery after prostatectomy.
problem Predicting sexual function recovery after prostatectomy surgery.
method Bayesian model using pre-treatment information.
result Personalized predictions of sexual function recovery.
Study improves machine learning for estimating survival treatment effects.
problem Estimating heterogeneous survival treatment effects in observational data.
method Flexible machine learning methods in the counterfactual framework, including AFT-BART-NP.
result AFT-BART-NP consistently yields best performance in terms of bias, precision, and frequentist coverage.
Over the past decades, statisticians and machine-learning researchers have developed literally thousands of new tools for the reduction of high-dimensional data in order to identify the variables most responsible for a particular trait. These tools have applications in a plethora of settings, including data analysis in…
New method groups genetic data into coherent topics for disease insights.
problem Analyzing large, multi-dimensional genetic data sets.
method Conditional Hierarchical Bayesian Tucker Decomposition for genetic data analysis.
result Our models are more coherent than baseline models.
Estimates cost savings from early cancer diagnosis.
problem Improving early cancer diagnosis to reduce treatment costs.
method Combining published cancer treatment cost estimates by stage with incidence rates by stage at diagnosis, and extrapolating to other cancer sites.
result Estimates U.S. national annual treatment cost-savings from early cancer diagnosis in the trillions.
VarDeepPCA: A Sampling-Free Variational DNN Plugin for OOD Segmentation with Uncertainty Estimation
problem Deep neural networks (DNNs) fail to generalize to out-of-distribution (OOD) medical images due to variations in scanners and acquisition protocols.
method VarDeepPCA is a lightweight variational DNN framework that learns a distribution of valid anatomical geometries using small in-distribution datasets.
result VarDeepPCA restores segmentation maps produced by existing methods on OOD data to improve anatomical plausibility and reduce errors.
DP synthetic data may inflate statistical test results, caution advised.
problem Inflated Type I errors in statistical tests on DP-synthetic data.
method Evaluation of Mann-Whitney U test, t-test, chi-squared test, and median test on DP-synthetic data generated from real-world and simulated datasets using various DP-synthetic data generation methods.
result A large portion of evaluation results showed inflated Type I errors, especially at low privacy levels.
The outcome of a functional genomics pipeline is usually a partial list of genomic features, ranked by their relevance in modelling biological phenotype in terms of a classification or regression model. Due to resampling protocols or just within a meta-analysis comparison, instead of one list it is often the case that …
Penalized regression models are popularly used in high-dimensional data analysis to conduct variable selection and model fitting simultaneously. Whereas success has been widely reported in literature, their performances largely depend on the tuning parameters that balance the trade-off between model fitting and model s…
Modified AUC improves CNN training by considering model confidence.
problem Improving binary classifier performance metrics.
method Proposes a modified AUC metric that incorporates model confidence into BCE loss for CNN training.
result Demonstrates improved performance on three datasets: MNIST, prostate MRI, and brain MRI.
Study uses NMF to reduce cancer microarray data dimensions.
problem High dimensionality of cancer microarray data hinders understanding.
method Used Non-negative Matrix Factorization (NMF) for dimensionality reduction.
result NMF achieves 98% classification accuracy.
Develops confidence intervals for Fine-Gray model with high-dimensional covariates.
problem Estimating regression coefficients in Fine-Gray model with many covariates and censoring.
method One-step bias-correction for regularized estimation, theoretical framework for partial likelihood, concentration results for time-dependent processes.
result Established confidence intervals for Fine-Gray model with high-dimensional covariates.
New method selects key features from millions of biological data points.
problem Scalability issue in feature selection for ultra-high dimensional biological data.
method Scaled up HSIC Lasso to handle millions of features.
result Achieves high accuracy with only 20 out of one million features.
CausalMix generates synthetic data with causal controls for mixed-type tables.
problem Synthetic data for causal inference with mixed-type and multimodal tabular data.
method CausalMix combines Gaussian latent priors with data-type-specific decoders for control over overlap, confounding, and treatment effect heterogeneity.
result CausalMix achieves state-of-the-art distributional metrics and stable causal control.
Bayesian neural networks improve uncertainty quantification with unlabelled data.
problem Over-confidence in predictions on covariate-shifted data.
method Approximate Bayesian inference using posterior regularisation with pseudo-labels from unlabelled data.
result Significant improvement in uncertainty quantification accuracy on covariate-shifted data.
New method estimates treatment effects in high-dimensional survival data.
problem Estimating causal effects for survival outcomes with many covariates.
method Orthogonal score method and Hazards Difference (HDi) estimator for high-dimensional additive hazards model.
result Valid inference for conditional treatment effect under high-dimensional setting.
Proposes a two-stage method for estimating heterogeneous treatment effects using gradient boosting trees.
problem Estimating heterogeneous treatment effects in randomized clinical trials with high-dimensional predictive markers.
method Two-stage statistical learning procedure using gradient boosting trees (XGBoost) to estimate main effects and HTE.
result Improves efficiency in estimating heterogeneous treatment effects through nonparametric function estimation.
Paper proposes clustering model for ICC based on histologic patterns.
problem Challenges in grading rare cancers like ICC due to small sample sizes and difficulty in extracting patterns.
method Unsupervised deep convolutional autoencoder clustering model trained on 246 ICC digitized slides.
result Three clusters significantly associated with recurrence-free survival in Cox-proportional hazard models.
This paper computes exact posterior distributions of mixture weights in hierarchical Bayesian models.
problem Uncertainty in class membership or data-generating processes in heterogeneous data.
method Exact marginalization of mixture weights using dynamic programming and FFT for two components, and joint dynamic program for K >= 3 components.
result Exact posterior distributions of mixture weights are finite mixtures of Beta distributions, providing credible intervals and per-observation local false-discovery rates.