Lesion-Aware Chest Radiography Abnormality Classification with Object Detection Framework

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

Autores

Pedrosa, J (Autor) Outra; Sousa, P (Autor) Outra; Silva, J (Autor) Outra; Ana Maria Mendonça (Autor) FEUP; Aurélio Campilho (Autor) FEUP

Ata de Conferência Internacional

Título: Proceedings - IEEE Symposium on Computer-Based Medical Systems

Páginas: 806-813

36th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2023

L¿Aquila, 22 June 2023 through 24 June 2023

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00Z-7AA

DOI: 10.1109/cbms58004.2023.00324

Abstract (EN): Chest radiography is one of the most ubiquitous medical imaging modalities. Nevertheless, the interpretation of chest radiography images is time-consuming, complex and subject to observer variability. As such, automated diagnosis systems for pathology detection have been proposed, aiming to reduce the burden on radiologists. The advent of deep learning has fostered the development of solutions for both abnormality detection with promising results. However, these tools suffer from poor explainability as the reasons that led to a decision cannot be easily understood, representing a major hurdle for their adoption in clinical practice. In order to overcome this issue, a method for chest radiography abnormality detection is presented which relies on an object detection framework to detect individual findings and thus separate normal and abnormal CXRs. It is shown that this framework is capable of an excellent performance in abnormality detection (AUC: 0.993), outperforming other state-of-the-art classification methodologies (AUC: 0.976 using the same classes). Furthermore, validation on external datasets shows that the proposed framework has a smaller drop in performance when applied to previously unseen data (21.9% vs 23.4% on average). Several approaches for object detection are compared and it is shown that merging pathology classes to minimize radiologist variability improves the localization of abnormal regions (0.529 vs 0.491 APF when using all pathology classes), resulting in a network which is more explainable and thus more suitable for integration in clinical practice.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 8