Introducing lifelong machine learning in the active tuberculosis classification through chest radiographs
Introducing lifelong machine learning in the active tuberculosis classification through chest radiographs
Data
2021
Autores
Alves, Regina Reis da Costa
Tavares, Frederico Caetano Jandre de Assis
Seixas, José Manoel de
Trajman, Anete
Journal Title
Journal ISSN
Volume Title
Publisher
Sociedade Brasileira de Inteligência Computacional
Resumo
Tuberculosis (TB) is a contagious disease which is
among the top 10 causes of death in the world. In order to
eliminate the disease by 2050, the treatment of TB infection (TBI)
is essential, which requires radiological reports to exclude active
tuberculosis. The automatic X-ray classifiers used today are based
on models that do not guarantee the retention of knowledge if
they need to learn new tasks over time. This work proposes the
introduction of the lifelong machine learning (LML) paradigm in
automatic X-ray classifiers aimed at helping to diagnose active
TB (ATB). Two LML algorithms, Efficient Lifelong Learning
Algorithm (ELLA) and Learning without Forgetting (LwF), are
applied to the TB and pneumonia classification tasks. The results
show that it is possible to keep the performance in both tasks
with the LML paradigm.
Description
Palavras-chave
lifelong learning, machine learning, tuberculosis, tuberculosis infection, latent tuberculosis, x-ray classification
Citação
Alves RRC, Tavares FCJA, Seixas JM, Trajman A. Introducing lifelong machine learning in the active tuberculosis classification through chest radiographs [Apresentação no XV Congresso Brasileiro de Inteligência Computacional: Artigos Apresentados nas Seções Técnicas CBIC; 2021, Joinville, SC, Brasil]. doi: 10.21528/CBIC2021-119.