Diagnostic Performance of Deep Learning Models for Gastric Intestinal Metaplasia Detection in Narrow-band Images

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

Autores

Martins, ML (Autor) Outra; Pedroso, M (Autor) Outra; Libânio, D (Autor) Outra; Dinis Ribeiro, M (Autor) Outra; Coimbra, M (Autor) FCUP; Renna, F (Autor) FCUP

Ata de Conferência Internacional

Título: 2023 45TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY, EMBC

Páginas: 1-4

45th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

Sydney, AUSTRALIA, JUL 24-27, 2023

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 1 Citação

Outras Informações

ID Authenticus: P-00Z-X4Z

DOI: 10.1109/embc40787.2023.10340055

Abstract (EN): Gastric Intestinal Metaplasia (GIM) is one of the precancerous conditions in the gastric carcinogenesis cascade and its optical diagnosis during endoscopic screening is challenging even for seasoned endoscopists. Several solutions leveraging pre-trained deep neural networks (DNNs) have been recently proposed in order to assist human diagnosis. In this paper, we present a comparative study of these architectures in a new dataset containing GIM and non-GIM Narrow-band imaging still frames. We find that the surveyed DNNs perform remarkably well on average, but still measure sizeable interfold variability during cross-validation. An additional ad-hoc analysis suggests that these baseline architectures may not perform equally well at all scales when diagnosing GIM.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 4