RadFormer: Transformers with global-local attention for interpretable and accurate Gallbladder Cancer detection.
Med Image Anal
; 83: 102676, 2023 01.
Article
em En
| MEDLINE
| ID: mdl-36455424
ABSTRACT
We propose a novel deep neural network architecture to learn interpretable representation for medical image analysis. Our architecture generates a global attention for region of interest, and then learns bag of words style deep feature embeddings with local attention. The global, and local feature maps are combined using a contemporary transformer architecture for highly accurate Gallbladder Cancer (GBC) detection from Ultrasound (USG) images. Our experiments indicate that the detection accuracy of our model beats even human radiologists, and advocates its use as the second reader for GBC diagnosis. Bag of words embeddings allow our model to be probed for generating interpretable explanations for GBC detection consistent with the ones reported in medical literature. We show that the proposed model not only helps understand decisions of neural network models but also aids in discovery of new visual features relevant to the diagnosis of GBC. Source-code is available at https//github.com/sbasu276/RadFormer.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Temas:
Geral
/
Tipos_de_cancer
/
Outros_tipos
Base de dados:
MEDLINE
Assunto principal:
Neoplasias da Vesícula Biliar
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Med Image Anal
Assunto da revista:
DIAGNOSTICO POR IMAGEM
Ano de publicação:
2023
Tipo de documento:
Article