AIMC Topic: Bayes Theorem

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A positive/unlabeled approach for the segmentation of medical sequences using point-wise supervision.

Medical image analysis
The ability to quickly annotate medical imaging data plays a critical role in training deep learning frameworks for segmentation. Doing so for image volumes or video sequences is even more pressing as annotating these is particularly burdensome. To a...

Application of a Poisson deep neural network model for the prediction of count data in genome-based prediction.

The plant genome
Genomic selection (GS) is revolutionizing conventional ways of developing new plants and animals. However, because it is a predictive methodology, GS strongly depends on statistical and machine learning to perform these predictions. For continuous ou...

Learning-to-augment strategy using noisy and denoised data: Improving generalizability of deep CNN for the detection of COVID-19 in X-ray images.

Computers in biology and medicine
Chest X-ray images are used in deep convolutional neural networks for the detection of COVID-19, the greatest human challenge of the 21st century. Robustness to noise and improvement of generalization are the major challenges in designing these netwo...

Copula-Based Data Augmentation on a Deep Learning Architecture for Cardiac Sensor Fusion.

IEEE journal of biomedical and health informatics
In the wake of Big Data, traditional Machine Learning techniques are now often integrated in the clinical workflow. Despite more capable, Deep Learning methods are not equally accepted given their unsatiated need for great amounts of training data an...

A hybrid machine learning/pharmacokinetic approach outperforms maximum a posteriori Bayesian estimation by selectively flattening model priors.

CPT: pharmacometrics & systems pharmacology
Model-informed precision dosing (MIPD) approaches typically apply maximum a posteriori (MAP) Bayesian estimation to determine individual pharmacokinetic (PK) parameters with the goal of optimizing future dosing regimens. This process combines knowled...

Bayesian Fully Convolutional Networks for Brain Image Registration.

Journal of healthcare engineering
The purpose of medical image registration is to find geometric transformations that align two medical images so that the corresponding voxels on two images are spatially consistent. Nonrigid medical image registration is a key step in medical image p...

Prioritization of zero-carbon measures for sustainable urban mobility using integrated double hierarchy decision framework and EDAS approach.

The Science of the total environment
Zero-carbon is the current buzzword triggering the minds of every people in the world. The current pandemic situation has given the world an alarm to act towards the reduction/eradication of carbon footprint. Developing countries like India are striv...

Bayesian Modeling for the Detection of Adverse Events Underreporting in Clinical Trials.

Drug safety
INTRODUCTION: Safety underreporting is a recurrent issue in clinical trials that can impact patient safety and data integrity. Clinical quality assurance (QA) practices used to detect underreporting rely on on-site audits; however, adverse events (AE...

AI in drug development: a multidisciplinary perspective.

Molecular diversity
The introduction of a new drug to the commercial market follows a complex and long process that typically spans over several years and entails large monetary costs due to a high attrition rate. Because of this, there is an urgent need to improve this...

Bayesian convolutional neural network estimation for pediatric pneumonia detection and diagnosis.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Pneumonia is a disease that affects the lungs, making breathing difficult. Nowadays, pneumonia is the disease that kills the most children under the age of five in the world, and if no action is taken, pneumonia is estimate...