Classification of colorectal tissue images from high throughput tissue microarrays by ensemble deep learning methods
Abstract Tissue microarray (TMA) core images are a treasure trove for artificial intelligence applications. However, a common problem of TMAs is multiple sectioning, which can change the content of the intended tissue core and requires re-labelling. Here, we investigate different ensemble methods fo...
Guardado en:
Autores principales: | Huu-Giao Nguyen, Annika Blank, Heather E. Dawson, Alessandro Lugli, Inti Zlobec |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/b59e9d18919b4f2499e27497bb7e8341 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Quick and Inexpensive Method to Elaborate Tissue Punches Useful in Paraffin Tissue Microarrays
por: García-Garza,Rubén, et al.
Publicado: (2013) -
Author Correction: Automated Gleason grading of prostate cancer tissue microarrays via deep learning
por: Eirini Arvaniti, et al.
Publicado: (2021) -
TIA-1 cytotoxic granule-associated RNA binding protein improves the prognostic performance of CD8 in mismatch repair-proficient colorectal cancer.
por: Inti Zlobec, et al.
Publicado: (2010) -
Comparison of microarray platforms for measuring differential microRNA expression in paired normal/cancer colon tissues.
por: Maurizio Callari, et al.
Publicado: (2012) -
High-Throughput Nano-Biofilm Microarray for Antifungal Drug Discovery
por: Anand Srinivasan, et al.
Publicado: (2013)