Breast Invasive Ductal Carcinoma Classification on Whole Slide Images with Weakly-Supervised and Transfer Learning
Invasive ductal carcinoma (IDC) is the most common form of breast cancer. For the non-operative diagnosis of breast carcinoma, core needle biopsy has been widely used in recent years for the evaluation of histopathological features, as it can provide a definitive diagnosis between IDC and benign les...
Guardado en:
Autores principales: | Fahdi Kanavati, Masayuki Tsuneki |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
MDPI AG
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/bedf7aee21894d6381efdee2e9c6cae4 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Deep Learning Models for Poorly Differentiated Colorectal Adenocarcinoma Classification in Whole Slide Images Using Transfer Learning
por: Masayuki Tsuneki, et al.
Publicado: (2021) -
Self supervised contrastive learning for digital histopathology
por: Ozan Ciga, et al.
Publicado: (2022) -
Ship Detection in Sentinel 2 Multi-Spectral Images with Self-Supervised Learning
por: Alina Ciocarlan, et al.
Publicado: (2021) -
Evaluation of semi-supervised learning using sparse labeling to segment cell nuclei
por: Bruch Roman, et al.
Publicado: (2020) -
Self-Supervised Deep Convolutional Neural Network for Chest X-Ray Classification
por: Matej Gazda, et al.
Publicado: (2021)