A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New method estimates optimal dose intervals for personalized treatment.
Proposes a method for generating prediction intervals in dose-response models using conformal prediction.
Shared Keyboard design improves phase I clinical trials by borrowing information across doses.
Develops a two-stage conformal prediction method for Parkinson's disease medication needs.
CASCADE improves uncertainty communication in Parkinson's disease medication management.
Kernel method optimizes personalized dose rules for patients.
We study the problem of finding the optimal dosage in early stage clinical trials through the multi-armed bandit lens. We advocate the use of the Thompson Sampling principle, a flexible algorithm that can accommodate different types of monotonicity assumptions on the toxicity and efficacy of the doses. For the simplest…
Determining the optimal initial dose for warfarin is a critically important task. Several factors have an impact on the therapeutic dose for individual patients, such as patients' physical attributes (Age, Height, etc.), medication profile, co-morbidities, and metabolic genotypes (CYP2C9 and VKORC1). These wide range f…
SEEDA optimizes dose allocation in clinical trials to balance efficacy and safety.
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…
Proposes a new method to measure and avoid harm in machine learning decisions.
A new model improves CT image quality from low-dose scans.
Proposes a framework for automated radiation therapy treatment planning with uncertainty quantification.
SDF-Bayes finds safe drug combinations safely, balancing optimism and caution.
Develops a new RL algorithm for medical treatment regimes.
A new cycleGAN architecture reduces memory and parameter requirements for low-dose CT denoising.
A novel dose-finding design for cancer clinical trials using level set estimation.
The paper tackles online learning problems with monotone arm sequences, achieving optimal or near-optimal regret bounds.
A ML-based method reconstructs 3D organ doses from 2D radiographs for pediatric abdominal radiotherapy.
New methods use RL and DA to improve dosing precision and reduce side effects.
The distribution of absorbed dose in radionuclide therapy with Lu 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…
Warfarin is one of the most commonly used oral blood anticoagulant agent in the world, the proper dose of Warfarin is difficult to establish not only because it is substantially variant among patients, but also adverse even severe consequences of taking an incorrect dose. Typical practice is to prescribe an initial dos…
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…
New framework for managing medical risks using convex responses.
DoSE improves OOD detection by estimating model probability density.
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…
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…
Due to the widespread use of positron emission tomography (PET) in clinical practice, the potential risk of PET-associated radiation dose to patients needs to be minimized. However, with the reduction in the radiation dose, the resultant images may suffer from noise and artifacts that compromise diagnostic performance.…
Gaussian surrogates improve Poisson imaging performance at low doses.
Develops a neural surrogate for proton dose calculation using Monte Carlo dropout uncertainty.
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…
Paper develops methods to estimate derivative of dose-response curve for continuous treatments.
The development of computed tomography (CT) image reconstruction methods that significantly reduce patient radiation exposure while maintaining high image quality is an important area of research in low-dose CT (LDCT) imaging. We propose a new penalized weighted least squares (PWLS) reconstruction method that exploits …
Computer-Aided-Diagnosis (CADx) systems assist radiologists with identifying and classifying potentially malignant pulmonary nodules on chest CT scans using morphology and texture-based (radiomic) features. However, radiomic features are sensitive to differences in acquisitions due to variations in dose levels and slic…
Exploratory cancer drug studies test multiple tumor cell lines against multiple candidate drugs. The goal in each paired (cell line, drug) experiment is to map out the dose-response curve of the cell line as the dose level of the drug increases. We propose Bayesian Tensor Filtering (BTF), a hierarchical Bayesian model …
Obtaining accurate and reliable images from low-dose computed tomography (CT) is challenging. Regression convolutional neural network (CNN) models that are learned from training data are increasingly gaining attention in low-dose CT reconstruction. This paper modifies the architecture of an iterative regression CNN, BC…
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…
Proposes estimators for complex dose-response curves using kernel methods.
A new algorithm uses concavity in Gaussian processes to optimize decisions in bandit problems.
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…
The challenge in controlling stochastic systems in which low-probability events can set the system on catastrophic trajectories is to develop a robust ability to respond to such events without significantly compromising the optimality of the baseline control policy. This paper presents CelluDose, a stochastic simulatio…
We study the problem of policy evaluation and learning from batched contextual bandit data when treatments are continuous, going beyond previous work on discrete treatments. Previous work for discrete treatment/action spaces focuses on inverse probability weighting (IPW) and doubly robust (DR) methods that use a reject…
Method bounds continuous-valued treatment effects when confounding variables are hidden.
A human-in-the-loop ML framework for precision dosing reduces expert workload and removes bias.
Proposes a method to estimate causal effects of continuous treatments using instrumental variables.
SHIFT improves robustness in estimating dose-response functions with heavy-tailed contamination.
The paper integrates AI and expert knowledge to optimize radiotherapy decisions.
The Lethal Dose Conjecture limits how much poisoned data can be tolerated.