VOLTA: an enVironment-aware cOntrastive ceLl represenTation leArning for histopathology.
Nat Commun
; 15(1): 3942, 2024 May 10.
Article
en En
| MEDLINE
| ID: mdl-38729933
ABSTRACT
In clinical oncology, many diagnostic tasks rely on the identification of cells in histopathology images. While supervised machine learning techniques necessitate the need for labels, providing manual cell annotations is time-consuming. In this paper, we propose a self-supervised framework (enVironment-aware cOntrastive cell represenTation learning VOLTA) for cell representation learning in histopathology images using a technique that accounts for the cell's mutual relationship with its environment. We subject our model to extensive experiments on data collected from multiple institutions comprising over 800,000 cells and six cancer types. To showcase the potential of our proposed framework, we apply VOLTA to ovarian and endometrial cancers and demonstrate that our cell representations can be utilized to identify the known histotypes of ovarian cancer and provide insights that link histopathology and molecular subtypes of endometrial cancer. Unlike supervised models, we provide a framework that can empower discoveries without any annotation data, even in situations where sample sizes are limited.
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Neoplasias Ováricas
/
Neoplasias Endometriales
Límite:
Female
/
Humans
Idioma:
En
Revista:
Nat Commun
Asunto de la revista:
BIOLOGIA
/
CIENCIA
Año:
2024
Tipo del documento:
Article
País de afiliación:
Canadá