AIMC Topic: Time Factors

Clear Filters Showing 331 to 340 of 2001 articles

Transformer-CNN hybrid network for improving PET time of flight prediction.

Physics in medicine and biology
In positron emission tomography (PET) reconstruction, the integration of time-of-flight (TOF) information, known as TOF-PET, has been a major research focus. Compared to traditional reconstruction methods, the introduction of TOF enhances the signal-...

Is deep learning-enabled real-time personalized CT dosimetry feasible using only patient images as input?

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: To propose a novel deep-learning based dosimetry method that allows quick and accurate estimation of organ doses for individual patients, using only their computed tomography (CT) images as input.

OrganoIDNet: a deep learning tool for identification of therapeutic effects in PDAC organoid-PBMC co-cultures from time-resolved imaging data.

Cellular oncology (Dordrecht, Netherlands)
PURPOSE: Pancreatic Ductal Adenocarcinoma (PDAC) remains a challenging disease due to its complex biology and aggressive behavior with an urgent need for efficient therapeutic strategies. To assess therapy response, pre-clinical PDAC organoid-based m...

Neural topic models with survival supervision: Jointly predicting time-to-event outcomes and learning how clinical features relate.

Artificial intelligence in medicine
We present a neural network framework for learning a survival model to predict a time-to-event outcome while simultaneously learning a topic model that reveals feature relationships. In particular, we model each subject as a distribution over "topics...

Fast synchronization control and application for encryption-decryption of coupled neural networks with intermittent random disturbance.

Neural networks : the official journal of the International Neural Network Society
In this paper, we design a new class of coupled neural networks with stochastically intermittent disturbances, in which the perturbation mechanism is different from other existed random neural networks. It is significant to construct the new models, ...

Predicting 1 year readmission for heart failure: A comparative study of machine learning and the LACE index.

ESC heart failure
AIMS: There is a lack of tools for accurately identifying the risk of readmission for heart failure in elderly patients with arrhythmia. The aim of this study was to establish and compare the performance of the LACE [length of stay ('L'), acute (emer...

Sliding mode control for uncertain fractional-order reaction-diffusion memristor neural networks with time delays.

Neural networks : the official journal of the International Neural Network Society
This paper investigates a sliding mode control method for a class of uncertain delayed fractional-order reaction-diffusion memristor neural networks. Different from most existing literature on sliding mode control for fractional-order reaction-diffus...

A robust multi-scale feature extraction framework with dual memory module for multivariate time series anomaly detection.

Neural networks : the official journal of the International Neural Network Society
Although existing reconstruction-based multivariate time series anomaly detection (MTSAD) methods have shown advanced performance, most assume the training data is clean. When faced with noise or contamination in training data, they can also reconstr...

Machine learning prediction of one-year mortality after percutaneous coronary intervention in acute coronary syndrome patients.

International journal of cardiology
BACKGROUND: Machine learning (ML) models have the potential to accurately predict outcomes and offer novel insights into inter-variable correlations. In this study, we aimed to design ML models for the prediction of 1-year mortality after percutaneou...