AIMC Topic: Reproducibility of Results

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U-Net Deep-Learning-Based 3D Cell Counter for the Quality Control of 3D Cell-Based Assays through Seed Cell Measurement.

SLAS technology
Conventional cell-counting software uses contour or watershed segmentations and focuses on identifying two-dimensional (2D) cells attached on the bottom of plastic plates. Recently developed software has been useful tools for the quality control of 2...

Numerical Spiking Neural P Systems.

IEEE transactions on neural networks and learning systems
Spiking neural P (SN P) systems are a class of discrete neuron-inspired computation models, where information is encoded by the numbers of spikes in neurons and the timing of spikes. However, due to the discontinuous nature of the integrate-and-fire ...

Framed and non-framed robotics in neurosurgery: A 10-year single-center experience.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: Safety, efficacy and efficiency of neurosurgical robots are defined by their design (i.e., framed and non-framed) and procedural workflow (PW) (from image to surgery). The present study describes the quality indicators of three different ...

Galaxy-ML: An accessible, reproducible, and scalable machine learning toolkit for biomedicine.

PLoS computational biology
Supervised machine learning is an essential but difficult to use approach in biomedical data analysis. The Galaxy-ML toolkit (https://galaxyproject.org/community/machine-learning/) makes supervised machine learning more accessible to biomedical scien...

Comparison of cephalometric measurements between conventional and automatic cephalometric analysis using convolutional neural network.

Progress in orthodontics
OBJECTIVE: The rapid development of artificial intelligence technologies for medical imaging has recently enabled automatic identification of anatomical landmarks on radiographs. The purpose of this study was to compare the results of an automatic ce...

The human-in-the-loop: an evaluation of pathologists' interaction with artificial intelligence in clinical practice.

Histopathology
AIMS: One of the major drivers of the adoption of digital pathology in clinical practice is the possibility of introducing digital image analysis (DIA) to assist with diagnostic tasks. This offers potential increases in accuracy, reproducibility, and...

i4mC-EL: Identifying DNA N4-Methylcytosine Sites in the Mouse Genome Using Ensemble Learning.

BioMed research international
As one of important epigenetic modifications, DNA N4-methylcytosine (4mC) plays a crucial role in controlling gene replication, expression, cell cycle, DNA replication, and differentiation. The accurate identification of 4mC sites is necessary to und...

Trust in Artificial Intelligence: Meta-Analytic Findings.

Human factors
OBJECTIVE: The present meta-analysis sought to determine significant factors that predict trust in artificial intelligence (AI). Such factors were divided into those relating to (a) the human trustor, (b) the AI trustee, and (c) the shared context of...

Deep Learning Improves the Temporal Reproducibility of Aortic Measurement.

Journal of digital imaging
Imaging-based measurements form the basis of surgical decision making in patients with aortic aneurysm. Unfortunately, manual measurement suffer from suboptimal temporal reproducibility, which can lead to delayed or unnecessary intervention. We teste...