A Bi-LSTM and multihead attention-based model incorporating radiomics signatures and radiological features for differentiating the main subtypes of lung adenocarcinoma

BACKGROUND: The radiological features of computed tomography (CT) images and the sequence of radiomics signatures in continuous slices of lung CT lesions are helpful in identifying subtypes of lung adenocarcinoma. A model based on bidirectional long short-term memory (Bi-LSTM) and multihead attentio...

Descripción completa

Detalles Bibliográficos
Autores principales: Ren, Jinjing, Chen, Ling, Xu, Huilin, Zheng, Xinlei, Ren, He
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2023
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10347360/
https://www.ncbi.nlm.nih.gov/pubmed/37456282
http://dx.doi.org/10.21037/qims-22-848

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10347360/
https://www.ncbi.nlm.nih.gov/pubmed/37456282
http://dx.doi.org/10.21037/qims-22-848

Ejemplares similares