Wide Residual Network for Lung-Rads (TM) Screening Referral

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2019

Autores

Carlos A. Ferreira (Autor) Outra; Guilherme Aresta (Autor) Outra; António Cunha (Autor) Outra; Ana Maria Mendonça (Autor) FEUP; Aurélio Campilho (Autor) FEUP

Ata de Conferência Internacional

Título: 6th IEEE Portuguese Meeting on Bioengineering, ENBENG 2019 - Proceedings

Páginas: 1-4

6th IEEE Portuguese Meeting on Bioengineering, ENBENG 2019

22 February 2019 through 23 February 2019

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 3 Citações

Outras Informações

ID Authenticus: P-00Q-FXN

DOI: 10.1109/enbeng.2019.8692560

Abstract (EN): Lung cancer has an increasing preponderance in worldwide mortality, demanding for the development of efficient screening methods. With this in mind, a binary classification method using Lung-RADS (TM) guidelines to warn changes in the screening management is proposed. First, having into account the lack of public datasets for this task, the lung nodules in the LIDC-IDRI dataset were re-annotated to include a Lung-RADS (TM)-based referral label. Then, a wide residual network is used for automatically assessing lung nodules in 3D chest computed tomography exams. Unlike the standard malignancy prediction approaches, the proposed method avoids the need to segment and characterize lung nodules, and instead directly defines if a patient should be submitted for further lung cancer tests. The system achieves a nodule-wise accuracy of 0.87 +/- 0.02.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 4