Refining dataset curation methods for deep learning-based automated tuberculosis screening.

ABSTRACT:

Background

The study objective was to determine whether unlabeled datasets can be used to further train and improve the accuracy of a deep learning system (DLS) for the detection of tuberculosis (TB) on chest radiographs (CXRs) using a two-stage semi-supervised approach.

Methods

A total of 111,622 CXRs from the National Institute of Health ChestX-ray14 database were collected. A cardiothoracic radiologist reviewed a subset of 11,000 CXRs and dichotomously labeled each for the presence or absence of potential TB findings; these interpretations were used to train a deep convolutional neural network (DCNN) to identify CXRs with possible TB (Phase I). The best performing algorithm was then used to label the remaining database consisting of 100,622 radiographs; subsequently, thes

SUBMITTER: Kim TK 

PROVIDER: S-EPMC7578485 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature