AIMC Topic: Humans

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A deep learning approach for sepsis monitoring via severity score estimation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Sepsis occurs in response to an infection in the body and can progress to a fatal stage. Detection and monitoring of sepsis require multi-step analysis, which is time-consuming, costly and requires medically trained personne...

The importance of standardisation - COVID-19 CT & Radiograph Image Data Stock for deep learning purpose.

Computers in biology and medicine
With the number of affected individuals still growing world-wide, the research on COVID-19 is continuously expanding. The deep learning community concentrates their efforts on exploring if neural networks can potentially support the diagnosis using C...

Deep learning reconstruction of equilibrium phase CT images in obese patients.

European journal of radiology
PURPOSE: To compare abdominal equilibrium phase (EP) CT images of obese and non-obese patients to identify the reconstruction method that preserves the diagnostic value of images obtained in obese patients.

Correlations Between Phenotypes and Biological Process Ontologies in Monogenic Human Diseases.

Interdisciplinary sciences, computational life sciences
A substantial body of research is focused to improve the understanding of the relationship between genotypes and phenotypes. Genotype-phenotype studies have shown promise in improving disease diagnosis in humans and identification of specific clinica...

Population coding in the cerebellum: a machine learning perspective.

Journal of neurophysiology
The cere resembles a feedforward, three-layer network of neurons in which the "hidden layer" consists of Purkinje cells (P-cells) and the output layer consists of deep cerebellar nucleus (DCN) neurons. In this analogy, the output of each DCN neuron i...

Semi-Supervised Medical Image Classification With Relation-Driven Self-Ensembling Model.

IEEE transactions on medical imaging
Training deep neural networks usually requires a large amount of labeled data to obtain good performance. However, in medical image analysis, obtaining high-quality labels for the data is laborious and expensive, as accurately annotating medical imag...

Layer Embedding Analysis in Convolutional Neural Networks for Improved Probability Calibration and Classification.

IEEE transactions on medical imaging
In this project, our goal is to develop a method for interpreting how a neural network makes layer-by-layer embedded decisions when trained for a classification task, and also to use this insight for improving the model performance. To do this, we fi...

Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images.

IEEE transactions on medical imaging
Nuclei mymargin segmentation is a fundamental task for various computational pathology applications including nuclei morphology analysis, cell type classification, and cancer grading. Deep learning has emerged as a powerful approach to segmenting nuc...

Acceleration Magnitude at Impact Following Loss of Balance Can Be Estimated Using Deep Learning Model.

Sensors (Basel, Switzerland)
Pre-impact fall detection can detect a fall before a body segment hits the ground. When it is integrated with a protective system, it can directly prevent an injury due to hitting the ground. An impact acceleration peak magnitude is one of key measur...