AIMC Topic: Reproducibility of Results

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Feasibility of Monte Carlo dropout-based uncertainty maps to evaluate deep learning-based synthetic CTs for adaptive proton therapy.

Medical physics
BACKGROUND: Deep learning has shown promising results to generate MRI-based synthetic CTs and to enable accurate proton dose calculations on MRIs. For clinical implementation of synthetic CTs, quality assurance tools that verify their quality and rel...

LFighter: Defending against the label-flipping attack in federated learning.

Neural networks : the official journal of the International Neural Network Society
Federated learning (FL) provides autonomy and privacy by design to participating peers, who cooperatively build a machine learning (ML) model while keeping their private data in their devices. However, that same autonomy opens the door for malicious ...

Establishing a culture of highly reliable quality care.

Surgery
Reliability is the likelihood that a process will perform a required function without failure, consistent over time and personnel changes. In the rapidly evolving healthcare landscape, reliably delivering excellent surgical care demands a comprehensi...

Can the generalizability issue of artificial intelligence be overcome? Pneumothorax detection algorithm.

Journal of investigative medicine : the official publication of the American Federation for Clinical Research
The generalizability of artificial intelligence (AI) models is a major issue in the field of AI applications. Therefore, we aimed to overcome the generalizability problem of an AI model developed for a particular center for pneumothorax detection usi...

SIMPLEX: Multiple phase-cycled bSSFP quantitative magnetization transfer imaging with physic-guided simulation learning of neural network.

NeuroImage
Most quantitative magnetization transfer (qMT) imaging methods require acquiring additional quantitative maps (such as T) for data fitting. A method based on multiple phase-cycled bSSFP was recently proposed to enable high-resolution 3D qMT imaging b...

Evaluation of deep learning-based feature selection for single-cell RNA sequencing data analysis.

Genome biology
BACKGROUND: Feature selection is an essential task in single-cell RNA-seq (scRNA-seq) data analysis and can be critical for gene dimension reduction and downstream analyses, such as gene marker identification and cell type classification. Most popula...

Outcome prediction of methadone poisoning in the United States: implications of machine learning in the National Poison Data System (NPDS).

Drug and chemical toxicology
Methadone is an opioid receptor agonist with a high potential for abuse. The current study aimed to compare different machine learning models to predict the outcomes following methadone poisoning. This six-year retrospective longitudinal study utiliz...

MRI Deep Learning-Based Automatic Segmentation of Interventricular Septum for Black-Blood Myocardial T2* Measurement in Thalassemia.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: The T2* value of interventricular septum is routinely reported for grading myocardial iron load in thalassemia major, and automatic segmentation of septum could shorten analysis time and reduce interobserver variability.

CyGate Provides a Robust Solution for Automatic Gating of Single Cell Cytometry Data.

Analytical chemistry
To gain a better understanding of the complex human immune system, it is necessary to measure and interpret numerous cellular protein expressions at the single cell level. Mass cytometry is a relatively new technology that offers unprecedented inform...