Deep Feature-Based Automated Chest Radiography Compliance Assessment

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2023

Autores

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

Ata de Conferência Internacional

Título: 2023 IEEE 7TH PORTUGUESE MEETING ON BIOENGINEERING, ENBENG

Páginas: 64-67

IEEE 7th Portuguese Meeting on Bioengineering (ENBENG)

Porto, PORTUGAL, JUN 22-23, 2023

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00Y-P07

DOI: 10.1109/enbeng58165.2023.10175341

Abstract (EN): Chest radiography is one of the most common imaging exams, but its interpretation is often challenging and timeconsuming, which has motivated the development of automated tools for pathology/abnormality detection. Deep learning models trained on large-scale chest X-ray datasets have shown promising results but are highly dependent on the quality of the data. However, these datasets often contain incorrect metadata and non-compliant or corrupted images. These inconsistencies are ultimately incorporated in the training process, impairing the validity of the results. In this study, a novel approach to detect non-compliant images based on deep features extracted from a patient position classification model and a pre-trained VGG16 model are proposed. This method is applied to CheXpert, a widely used public dataset. From a pool of 100 images, it is shown that the deep feature-based methods based on a patient position classification model are able to retrieve a larger number of non-compliant images (up to 81% of non-compliant images), when compared to the same methods but based on a pretrained VGG16 (up to 73%) and the state of the art uncertainty-based method (50%).

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 4