AIMC Topic: Reproducibility of Results

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Unsupervised machine learning for identifying important visual features through bag-of-words using histopathology data from chronic kidney disease.

Scientific reports
Pathologists use visual classification to assess patient kidney biopsy samples when diagnosing the underlying cause of kidney disease. However, the assessment is qualitative, or semi-quantitative at best, and reproducibility is challenging. To discov...

Object-Based Reliable Visual Navigation for Mobile Robot.

Sensors (Basel, Switzerland)
Visual navigation is of vital importance for autonomous mobile robots. Most existing practical perception-aware based visual navigation methods generally require prior-constructed precise metric maps, and learning-based methods rely on large training...

Artificial intelligence can detect left ventricular dilatation on contrast-enhanced thoracic computer tomography relative to cardiac magnetic resonance imaging.

The British journal of radiology
OBJECTIVES: To assess the diagnostic accuracy of an automated algorithm to detect left ventricular (LV) dilatation on non-ECG gated CT, using cardiac magnetic resonance (CMR) as reference standard.

SDFormer: A Novel Transformer Neural Network for Structural Damage Identification by Segmenting the Strain Field Map.

Sensors (Basel, Switzerland)
Damage identification is a key problem in the field of structural health monitoring, which is of great significance to improve the reliability and safety of engineering structures. In the past, the structural strain damage identification method based...

Automatic Deep-Learning Segmentation of Epicardial Adipose Tissue from Low-Dose Chest CT and Prognosis Impact on COVID-19.

Cells
Background: To develop a deep-learning (DL) pipeline that allowed an automated segmentation of epicardial adipose tissue (EAT) from low-dose computed tomography (LDCT) and investigate the link between EAT and COVID-19 clinical outcomes. Methods: This...

Deep learning models in medical image analysis.

Journal of oral biosciences
BACKGROUND: Deep learning is a state-of-the-art technology that has rapidly become the method of choice for medical image analysis. Its fast and robust object detection, segmentation, tracking, and classification of pathophysiological anatomical stru...

Predicting chemical hazard across taxa through machine learning.

Environment international
We applied machine learning methods to predict chemical hazards focusing on fish acute toxicity across taxa. We analyzed the relevance of taxonomy and experimental setup, showing that taking them into account can lead to considerable improvements in ...

Design of Fault Prediction System for Electromechanical Sensor Equipment Based on Deep Learning.

Computational intelligence and neuroscience
With the increasing complexity, scale, and intelligentization of modern equipment, the maintenance cost of equipment is increasing day by day. Moreover, once an unexpected major failure occurs, it will cause loss and damage to production, economy, an...

Classification of Ear Imagery Database using Bayesian Optimization based on CNN-LSTM Architecture.

Journal of digital imaging
The external and middle ear conditions are diagnosed using a digital otoscope. The clinical diagnosis of ear conditions is suffered from restricted accuracy due to the increased dependency on otolaryngologist expertise, patient complaint, blurring of...

Convolutional neural network-based computer-aided diagnosis in Hiesho (cold sensation).

Computers in biology and medicine
Hiesho (cold sensation) is a worldwide health problem primarily occurring in women. Females who suffered from Hiesho reported cold feeling at the extremities, which was also related to other chronic diseases. However, the diagnosis of Hiesho is still...