Shared Keyboard design improves phase I clinical trials by borrowing information across doses.
problem Interim decisions based on current dose data may overlook signals from neighboring doses.
method Bayesian model-assisted design using Beta kernel process with kernel-weighted pseudo-counts.
result Significant improvements in identifying maximum tolerated dose and safety.
SEEDA optimizes dose allocation in clinical trials to balance efficacy and safety.
problem Complex relationships between efficacy and toxicity in new drug trials.
method Adaptive clinical trial methodology that maximizes cumulative efficacy while ensuring safety constraints.
result SEEDA outperforms existing methods in finding optimal doses with higher success rates and fewer patients.
Develops a neural surrogate for proton dose calculation using Monte Carlo dropout uncertainty.
problem Computational demand in proton therapy workflows requiring repeated evaluations.
method Integrates Monte Carlo dropout into a neural network surrogate for fast, differentiable dose predictions and uncertainty quantification.
result Shows significant speedups over MC while retaining uncertainty information.
A new cycleGAN architecture reduces memory and parameter requirements for low-dose CT denoising.
problem Efficient unsupervised low-dose CT denoising with minimal memory and parameter usage.
method Single switchable generator using AdaIN layers for efficient training and inference.
result The proposed method outperforms previous cycleGAN approaches with half the parameters.
Bayesian model for cancer drug studies maps dose-response curves.
problem Mapping dose-response curves in cancer drug studies.
method Bayesian Tensor Filtering (BTF) with low-dimensional embeddings and structured shrinkage priors.
result BTF outperforms state-of-the-art methods in cancer drug studies.
Prototype controls stochastic drug resistance in cells.
problem Emergence of drug-resistant cells from random mutations.
method Deep reinforcement learning for adaptive drug dosing.
result 100% success rate in suppressing cell proliferation.
Develops a new RL algorithm for medical treatment regimes.
problem Optimal dose determination in continuous action environments.
method Quasi-optimal learning algorithm for near-optimal actions.
result Guaranteed convergence and effectiveness in real applications.
CASCADE improves uncertainty communication in Parkinson's disease medication management.
problem Uncertainty in clinical decision-making for Parkinson's disease patients.
method CASCADE uses a novel conformal prediction framework to adaptively scale prediction intervals based on classification uncertainty.
result CASCADE produces more efficient and robust prediction intervals for Parkinson's disease patients.
Prototype for adaptive electron microscopy scans reduces dose and time.
problem Reduce electron microscopy scan time and dose with minimal loss.
method Adaptive partial scanning with reinforcement learning.
result Reinforcement learning trained neural network optimizes scan paths.
Kernel method optimizes personalized dose rules for patients.
problem Finding optimal individualized dose rules for patients.
method Kernel assisted learning method for estimating optimal dose rules.
result The method identifies the optimal individualized dose rule and produces favorable outcomes.
Develops deep jump learning for continuous treatment OPE.
problem Estimating mean outcomes under new treatment rules using historical data from different rules.
method Adaptive deep discretization of continuous treatment space using deep learning and multi-scale change point detection.
result Validated method through theoretical results, simulations, and real application to Warfarin Dosing.
Proposes a framework for automated radiation therapy treatment planning with uncertainty quantification.
problem Quantifying uncertainties in dose-related quantities for automated treatment planning.
method Three-step pipeline: feature extraction, dose statistic prediction, and dose mimicking.
result Probabilistic treatment plans agree better with clinical counterparts than non-probabilistic ones.
Study uses Thompson Sampling to find optimal drug dosages in clinical trials.
problem Finding the best drug dosage in early-stage clinical trials.
method Adopted Thompson Sampling for dose-finding clinical trials, considering different monotonicity assumptions.
result Thompson Sampling variants outperform existing methods in various dose-finding scenarios.
New method estimates optimal dose intervals for personalized treatment.
problem Learning optimal dose intervals from observational data.
method Probability dose interval (PDI) method using DC algorithm.
result Consistent policy with risk converging to best-in-class at root-n rate.
Proposes a method to estimate causal effects of continuous treatments using instrumental variables.
problem Estimating causal effects of continuous treatments in the presence of unmeasured confounders.
method Introduces a novel framework using instrumental variables and a uniform regular weighting function to identify and estimate average dose-response functions.
result Establishes the asymptotic properties of the proposed methods for estimating average dose-response functions.
A model predicts which patients can safely use a warfarin dosing algorithm.
problem Determining the optimal initial dose for warfarin patients.
method Support Vector Machines with a polynomial kernel function.
result The model increases dosing accuracy by 15% in RMSE and 17% in MAE.
Proposes a method for generating prediction intervals in dose-response models using conformal prediction.
problem Uncertainty quantification in continuous treatments for personalized healthcare decisions.
method Causal dose-response problem framed as covariate shift, using weighted conformal prediction with propensity estimation and kernel functions.
result Demonstrates the significance of covariate shift assumptions for robust prediction intervals.
Warfarin dosing remains challenging due to narrow therapeutic index and highly individual variability. Incorrect warfarin dosing is associated with devastating adverse events. Remarkable efforts have been made to develop the machine learning based warfarin dosing algorithms incorporating clinical factors and genetic va…
New CycleGAN uses invertible generator for faster, less resource-intensive CT denoising.
problem Efficient unsupervised CT denoising without paired data.
method Single generator with wavelet residual domain, no discriminators, cycle consistency via invertible generator.
result Significantly improved denoising performance with faster training and less parameters.
A novel dose-finding design for cancer clinical trials using level set estimation.
problem Finding the maximum tolerated dose (MTD) in phase I cancer clinical trials.
method Proposes a novel dose-finding design based on level set estimation (LSE) to determine the next dose.
result The proposed LSE design achieves higher accuracy in estimating the MTD and lower risk of overdosing compared to existing designs.
A ML-based method reconstructs 3D organ doses from 2D radiographs for pediatric abdominal radiotherapy.
problem Reconstructing detailed 3D dose distributions for childhood cancer survivors using limited 2D radiographs.
method Surrogate-free ML approach using 142 abdominal planning CTs, 300 artificial plans, and evolutionary algorithm.
result Accurate 3D dose reconstructions with MAEs ≤ 1.7 Gy for edge organs, validated on independent dataset.
New methods use RL and DA to improve dosing precision and reduce side effects.
problem Improving precision and safety of anticancer drug dosing.
method Bayesian data assimilation and reinforcement learning.
result Significantly reduces incidence of severe side effects.
The distribution of absorbed dose in radionuclide therapy with Lu177 can be approximated by convolving an image of the time-integrated activity distribution with a dose voxel kernel representing different tissue types. This fast but inaccurate approximation is unsuitable for personalised dosimetry because it negle…
Proposes a new method for estimating non-pathwise differentiable functional parameters.
problem Estimating dose-response curves for continuous exposure.
method Targeted Highly Adaptive Lasso (HAL) for non-pathwise differentiable functional parameters.
result The Targeted HAL-MLE achieves dimension-free rates up to log(n) factors and outperforms other methods in simulations.
In coronary CT angiography, a series of CT images are taken at different levels of radiation dose during the examination. Although this reduces the total radiation dose, the image quality during the low-dose phases is significantly degraded. To address this problem, here we propose a novel semi-supervised learning tech…
SUPER learning combines supervised and unsupervised methods for LDCT image reconstruction.
problem Low-dose CT image reconstruction challenges.
method Combines supervised and unsupervised learning methods.
result SUPER learning dramatically outperforms constituent methods.
This study uses bandit algorithms to predict Warfarin dosages more accurately.
problem Determining the correct initial Warfarin dosage is challenging due to patient variability and adverse effects.
method Developed and evaluated linear bandit algorithms on real data from PharmGKB.
result Proposed algorithms outperformed fixed-dose and clinical algorithms.
BCD-Net improves low-dose CT image reconstruction.
problem Challenges in obtaining accurate low-dose CT images.
method Modified iterative regression CNN, BCD-Net, with faster numerical solvers.
result BCD-Net achieves better image quality and generalization than state-of-the-art methods.
Proposes a new method to measure and avoid harm in machine learning decisions.
problem Measuring and avoiding harm in machine learning algorithms.
method Formal definition of harm and benefit using causal models, counterfactual objective functions.
result Demonstrates that standard machine learning methods can lead to harmful policies under distributional shifts.
DoSE improves OOD detection by estimating model probability density.
problem Poor specificity of model likelihoods for OOD detection.
method DoSE uses density of states concept to avoid direct model probability comparison.
result DoSE achieves state-of-the-art performance on OOD detection benchmarks.
Estimating what would be an individual's potential response to varying levels of exposure to a treatment is of high practical relevance for several important fields, such as healthcare, economics and public policy. However, existing methods for learning to estimate counterfactual outcomes from observational data are ei…
PT-WGAN reduces PET image noise without losing details.
problem Low-dose PET images suffer from noise and artifacts.
method Parameter-transferred Wasserstein GAN for denoising.
result PT-WGAN outperforms state-of-the-art methods in denoising.
Gaussian surrogates improve Poisson imaging performance at low doses.
problem Improving Poisson imaging performance at low doses.
method Analysis of Poisson and Gaussian surrogate reconstruction objectives under Poisson noise.
result Gaussian surrogates can achieve MSE comparable to Poisson MAP at low doses.
Model based iterative reconstruction (MBIR) algorithms for low-dose X-ray CT are computationally expensive. To address this problem, we recently proposed a deep convolutional neural network (CNN) for low-dose X-ray CT and won the second place in 2016 AAPM Low-Dose CT Grand Challenge. However, some of the texture were n…
Many modern data sets are sampled with error from complex high-dimensional surfaces. Methods such as tensor product splines or Gaussian processes are effective/well suited for characterizing a surface in two or three dimensions but may suffer from difficulties when representing higher dimensional surfaces. Motivated by…
Paper develops methods to estimate derivative of dose-response curve for continuous treatments.
problem Estimating the derivative of the dose-response curve for continuous treatments.
method Doubly robust (DR) inference method using kernel smoothing, bias-corrected IPW and DR estimators.
result Proposes novel bias-corrected IPW and DR estimators for continuous treatments.
A new model improves CT image quality from low-dose scans.
problem Improving CT image quality from low-dose scans.
method Multi-layer Residual Sparsifying Transform (MRST) learning model for low-dose CT reconstruction.
result The MRST model outperforms conventional methods in maintaining subtle details.
Controlled interventions provide the most direct source of information for learning causal effects. In particular, a dose-response curve can be learned by varying the treatment level and observing the corresponding outcomes. However, interventions can be expensive and time-consuming. Observational data, where the treat…
GAN normalizes CT scans for consistent radiomic feature values.
problem Variations in dose levels and slice thickness affect radiomic features sensitivity.
method Used a 3D generative adversarial network (GAN) to normalize reduced dose, thick slice images to normal dose, thinner slice images.
result GAN-based approach led to significantly smaller error in radiomic features.
Proposes estimators for complex dose-response curves using kernel methods.
problem Estimating complex dose-response curves with continuous treatments, mediators, and covariates.
method Kernel ridge regression with sequential kernel embedding technique.
result Simple estimators for mediated and time-varying dose response curves with nonasymptotic uniform rates.
Image denoising techniques are essential to reducing noise levels and enhancing diagnosis reliability in low-dose computed tomography (CT). Machine learning based denoising methods have shown great potential in removing the complex and spatial-variant noises in CT images. However, some residue artifacts would appear in…
Method bounds continuous-valued treatment effects when confounding variables are hidden.
problem Inferring causal effects of continuous treatments when hidden confounders are present.
method Novel methodology to bound average and conditional average continuous-valued treatment effects.
result Method gives tighter coverage of true dose-response curve than existing methods.
A human-in-the-loop ML framework for precision dosing reduces expert workload and removes bias.
problem High cost of data annotation and lack of appropriate data for ML models.
method Incorporates human experts into the model learning loop to improve interpretability and reduce bias.
result The approach learns interpretable rules from data and potentially lowers expert workload.
C3T-Budget optimizes drug efficacy in dose-finding trials with budget and safety constraints.
problem Heterogeneous patient populations and budget constraints make dose-finding clinical trials challenging.
method Contextual constrained clinical trial algorithm that maximizes drug efficacy while learning subgroup responses.
result Demonstrates efficient budget usage and balanced learning-treatment trade-off in simulated trials.
SDF-Bayes finds safe drug combinations safely, balancing optimism and caution.
problem Finding safe drug combinations in clinical trials with multiple drugs and patient heterogeneity.
method SDF-Bayes uses Bayesian statistics to choose the most likely MTD while ensuring safety constraints.
result SDF-Bayes outperforms existing methods in both accuracy and safety for drug combination trials.
New framework for managing medical risks using convex responses.
problem Medical risk management and dosing optimization.
method Analyzes convex and concave dose-response functions, defines antifragility.
result Proposes a mathematical framework for integrating nonlinearities in oncology.
Public dataset for benchmarking deep learning CT reconstruction methods.
problem Lack of a fair benchmark for comparing deep learning CT reconstruction methods.
method Processed and simulated over 40,000 CT scan slices from the LIDC/IDRI Database.
result First baseline results provided for comparison.
A major challenge in computed tomography (CT) is to reduce X-ray dose to a low or even ultra-low level while maintaining the high quality of reconstructed images. We propose a new method for CT reconstruction that combines penalized weighted-least squares reconstruction (PWLS) with regularization based on a sparsifying…