A machine-learning expert-supporting system for diagnosis prediction of lymphoid neoplasms using a probabilistic decision-tree algorithm and immunohistochemistry profile database
Background Immunohistochemistry (IHC) has played an essential role in the diagnosis of hematolymphoid neoplasms. However, IHC interpretations can be challenging in daily practice, and exponentially expanding volumes of IHC data are making the task increasingly difficult. We therefore developed a mac...
Saved in:
Main Authors: | Yosep Chong (Author), Ji Young Lee (Author), Yejin Kim (Author), Jingyun Choi (Author), Hwanjo Yu (Author), Gyeongsin Park (Author), Mee Yon Cho (Author), Nishant Thakur (Author) |
---|---|
Format: | Book |
Published: |
Korean Society of Pathologists & the Korean Society for Cytopathology,
2020-11-01T00:00:00Z.
|
Subjects: | |
Online Access: | Connect to this object online. |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Diagnosis prediction of tumours of unknown origin using ImmunoGenius, a machine learning-based expert system for immunohistochemistry profile interpretation
by: Yosep Chong, et al.
Published: (2021) -
Artificial intelligence and classification of mature lymphoid neoplasms
by: Joaquim Carreras, et al.
Published: (2024) -
Colorectal epithelial neoplasm associated with gut-associated lymphoid tissue
by: Yo Han Jeon, et al.
Published: (2020) -
The validity of immunohistochemistry in detecting microsatellite instability in pediatric solid neoplasms
by: Khaldoon Aljerian, et al.
Published: (2024) -
An algorithm to generate correlated input-parameters to be used in probabilistic sensitivity analyses
by: Mohamed Neine, et al.
Published: (2021)