Machine Learning based sampling of X-Ray images for a computer-aided detection of Tuberculosis*
Machine Learning based sampling of X-Ray images for a computer-aided detection of Tuberculosis*
Arquivos
Data
2021
Autores
Ferreira, Fernando
Gaspar, Philipp
Oliveira, Lukas Müller de
Torres, Rodrigo
Araújo, Micael Veríssimo de
Covas, Carlos Eduardo
Bastos, Mayara
Trajamn, Anete
Seixas, José Manoel de
Journal Title
Journal ISSN
Volume Title
Publisher
Sociedade Brasileira de Inteligência Computacional
Resumo
Computer Aided Detection software relies on an
annotated data set of X-rays to be developed. The annotation
task requires extensive know-how and it is very time-consuming.
This work presents a sampling method to select the most relevant
images which will be annotated for the development of Tubercu-
losis screening platform based on machine learning algorithms.
The sampling task optimizes the annotation process by reducing
the number of images to be analyzed without compromising the
diversity and the significance power of the images in the dataset.
In this context, the image relevance is based on similarity and
dissimilarity measurements. The experiment consisted in a deep
learning feature engineering step, followed by topological analysis
based on Self-Organizing Map and K-Means.
Description
Palavras-chave
Deep Learning, CNN, SOM, Clustering, CAD.
Citação
Ferreira F, Gaspar P, Oliveira LM, Torres R, Araújo MV, Covas CE, Bastos M, Trajman A, Seixas JM. Machine Learning based sampling of X-Ray images for a computer-aided detection of Tuberculosis* [Aprasentação no XV Congresso Brasileiro de Inteligência Computacional: Artigos Apresentados nas Sessões Técnicas CBIC 2021, Joinville, SC, Brasil]. doi: 10.21528/CBIC2021-140.