Deep learning for computer-aided abnormalities classification in digital mammogram: A data-centric perspective.
Curr Probl Diagn Radiol
; 53(3): 346-352, 2024.
Article
em En
| MEDLINE
| ID: mdl-38302303
ABSTRACT
Breast cancer is the most common type of cancer in women, and early abnormality detection using mammography can significantly improve breast cancer survival rates. Diverse datasets are required to improve the training and validation of deep learning (DL) systems for autonomous breast cancer diagnosis. However, only a small number of mammography datasets are publicly available. This constraint has created challenges when comparing different DL models using the same dataset. The primary contribution of this study is the comprehensive description of a selection of currently available public mammography datasets. The information available on publicly accessible datasets is summarized and their usability reviewed to enable more effective models to be developed for breast cancer detection and to improve understanding of existing models trained using these datasets. This study aims to bridge the existing knowledge gap by offering researchers and practitioners a valuable resource to develop and assess DL models in breast cancer diagnosis.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Neoplasias da Mama
/
Aprendizado Profundo
Limite:
Female
/
Humans
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article