Cargando…
HistoPerm: A permutation-based view generation approach for improving histopathologic feature representation learning
Deep learning has been effective for histology image analysis in digital pathology. However, many current deep learning approaches require large, strongly- or weakly labeled images and regions of interest, which can be time-consuming and resource-intensive to obtain. To address this challenge, we pr...
Autores principales: | DiPalma, Joseph, Torresani, Lorenzo, Hassanpour, Saeed |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10339175/ https://www.ncbi.nlm.nih.gov/pubmed/37457594 http://dx.doi.org/10.1016/j.jpi.2023.100320 |
Ejemplares similares
-
ProgPerm: Progressive permutation for a dynamic representation of the robustness of microbiome discoveries
por: Zhang, Liangliang, et al.
Publicado: (2021) -
HistoML, a markup language for representation and exchange of histopathological features in pathology images
por: Lou, Peiliang, et al.
Publicado: (2022) -
Representations of permutation groups
por: Kerber, Adalbert
Publicado: (1971) -
Representations of permutation groups
por: Kerber, Adalbert
Publicado: (1975) -
Ultraspecific live imaging of the dynamics of zebrafish neutrophil granules by a histopermeable fluorogenic benzochalcone probe
por: Colucci-Guyon, Emma, et al.
Publicado: (2019)