Aggregation of cohorts for histopathological diagnosis with deep morphological analysis
Abstract There have been substantial efforts in using deep learning (DL) to diagnose cancer from digital images of pathology slides. Existing algorithms typically operate by training deep neural networks either specialized in specific cohorts or an aggregate of all cohorts when there are only a few...
Guardado en:
Autores principales: | Jeonghyuk Park, Yul Ri Chung, Seo Taek Kong, Yeong Won Kim, Hyunho Park, Kyungdoc Kim, Dong-Il Kim, Kyu-Hwan Jung |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/cc3a6ff7b5f74169a3a27530a75eb747 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Clinically accurate diagnosis of Alzheimer’s disease via multiplexed sensing of core biomarkers in human plasma
por: Kayoung Kim, et al.
Publicado: (2020) -
Role of CXCL10 in the progression of in situ to invasive carcinoma of the breast
por: Milim Kim, et al.
Publicado: (2021) -
Wear Behavior of Commercial Copper-Based Aircraft Brake Pads Fabricated under Different SPS Conditions
por: Kyung Il Kim, et al.
Publicado: (2021) -
Prognostic implications of regression of metastatic axillary lymph nodes after neoadjuvant chemotherapy in patients with breast cancer
por: Yul Ri Chung, et al.
Publicado: (2021) -
Loeffler's syndrome in a child: A rare radiological and histopathological diagnosis
por: Kiem Hao Tran, A/Prof, PhD, et al.
Publicado: (2022)