Medical decision support system using weakly-labeled lung CT scans.

ABSTRACT:

Purpose

Determination and development of an effective set of models leveraging Artificial Intelligence techniques to generate a system able to support clinical practitioners working with COVID-19 patients. It involves a pipeline including classification, lung and lesion segmentation, as well as lesion quantification of axial lung CT studies.

Approach

A deep neural network architecture based on DenseNet is introduced for the classification of weakly-labeled, variable-sized (and possibly sparse) axial lung CT scans. The models are trained and tested on aggregated, publicly available data sets with over 10 categories. To further assess the models, a data set was collected from multiple medical institutions in Colombia, which includes healthy, COVID-19 and patients with other di

SUBMITTER: Murillo-Gonzalez A 

PROVIDER: S-EPMC9554434 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature