Cargando…
Unsupervised Learning Based on Multiple Descriptors for WSIs Diagnosis
An automatic pathological diagnosis is a challenging task because histopathological images with different cellular heterogeneity representations are sometimes limited. To overcome this, we investigated how the holistic and local appearance features with limited information can be fused to enhance th...
Autores principales: | Sheikh, Taimoor Shakeel, Kim, Jee-Yeon, Shim, Jaesool, Cho, Migyung |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9222016/ https://www.ncbi.nlm.nih.gov/pubmed/35741289 http://dx.doi.org/10.3390/diagnostics12061480 |
Ejemplares similares
-
WSIS highlights
Publicado: (2004) -
Histopathological Classification of Breast Cancer Images Using a Multi-Scale Input and Multi-Feature Network
por: Sheikh, Taimoor Shakeel, et al.
Publicado: (2020) -
WSIS report from Geneva
Publicado: (2005) -
Internet compromise clears way for WSIS agreement
por: Ermert, M
Publicado: (2003) -
Professor Atta invited to attend WSIS as `eminent scientist'
Publicado: (2003)