AIMC Topic: Databases, Factual

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Imaging Database Preparation for Machine Learning.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes

CoLe-CNN: Context-learning convolutional neural network with adaptive loss function for lung nodule segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: An accurate segmentation of lung nodules in computed tomography images is a crucial step for the physical characterization of the tumour. Being often completely manually accomplished, nodule segmentation turns to be a tediou...

Finding, visualizing, and quantifying latent structure across diverse animal vocal repertoires.

PLoS computational biology
Animals produce vocalizations that range in complexity from a single repeated call to hundreds of unique vocal elements patterned in sequences unfolding over hours. Characterizing complex vocalizations can require considerable effort and a deep intui...

The parable of arable land: Characterizing large scale land acquisitions through network analysis.

PloS one
Land is a scarce resource and its depletion is related to a combination of demographic and economic factors. Hence, the changes in dietary habits and increase in world population that upturn the food demand, are intertwined with a context of increasi...

Unsupervised learning for large-scale corneal topography clustering.

Scientific reports
Machine learning algorithms have recently shown their precision and potential in many different use cases and fields of medicine. Most of the algorithms used are supervised and need a large quantity of labeled data to achieve high accuracy. Also, mos...

The covariance perceptron: A new paradigm for classification and processing of time series in recurrent neuronal networks.

PLoS computational biology
Learning in neuronal networks has developed in many directions, in particular to reproduce cognitive tasks like image recognition and speech processing. Implementations have been inspired by stereotypical neuronal responses like tuning curves in the ...

A comprehensive study on classification of COVID-19 on computed tomography with pretrained convolutional neural networks.

Scientific reports
The use of imaging data has been reported to be useful for rapid diagnosis of COVID-19. Although computed tomography (CT) scans show a variety of signs caused by the viral infection, given a large amount of images, these visual features are difficult...

Utilization of Deep Learning for Subphenotype Identification in Sepsis-Associated Acute Kidney Injury.

Clinical journal of the American Society of Nephrology : CJASN
BACKGROUND AND OBJECTIVES: Sepsis-associated AKI is a heterogeneous clinical entity. We aimed to agnostically identify sepsis-associated AKI subphenotypes using deep learning on routinely collected data in electronic health records.

Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification.

BMC bioinformatics
BACKGROUND: As a machine learning method with high performance and excellent generalization ability, extreme learning machine (ELM) is gaining popularity in various studies. Various ELM-based methods for different fields have been proposed. However, ...

The data dimensionality reduction and bad data detection in the process of smart grid reconstruction through machine learning.

PloS one
To detect false data injection attacks (FDIAs) in power grid reconstruction and solve the problem of high data dimension and bad abnormal data processing in the power system, thereby achieving safe and stable operation of the power grid system, this ...