Attention-driven Spatial Transformer Network for Abnormality Detection in Chest X-Ray Images

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2022

Autores

Rocha, J (Autor) Outra; Pereira, SC (Autor) Outra; Pedrosa, J (Autor) Outra; Aurélio Campilho (Autor) FEUP; Ana Maria Mendonça (Autor) FEUP

Ata de Conferência Internacional

Título: 2022 IEEE 35TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS)

Páginas: 252-257

35th IEEE International Symposium on Computer-Based Medical Systems (CBMS)

ELECTR NETWORK, JUL 21-23, 2022

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00X-3V0

DOI: 10.1109/cbms55023.2022.00051

Abstract (EN): Backed by more powerful computational resources and optimized training routines, deep learning models have attained unprecedented performance in extracting information from chest X-ray data. Preceding other tasks, an automated abnormality detection stage can be useful to prioritize certain exams and enable a more efficient clinical workflow. However, the presence of image artifacts such as lettering often generates a harmful bias in the classifier, leading to an increase of false positive results. Consequently, healthcare would benefit from a system that selects the thoracic region of interest prior to deciding whether an image is possibly pathologic. The current work tackles this binary classification exercise using an attention-driven and spatially unsupervised Spatial Transformer Network (STN). The results indicate that the STN achieves similar results to using YOLO-cropped images, with fewer computational expenses and without the need for localization labels. More specifically, the system is able to distinguish between normal and abnormal CheXpert images with a mean AUC of 84.22%.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6