Label Extraction and AI for Digital Pathology
Tissue-based studies
generate large amounts of histology data containing important biological
information in the form of imagery and metadata. These digital
pathology slides are labeled using text and barcodes for their identification. The
older technologies used printed or handwritten labels for specimen
labeling. The Label
Extraction Solution uses state-of-the-art OCR
technologies, image processing, and AI to read, understand, and store label data from digital pathology
slides. Additional manual validation of the data leads to a highly automated
process which reduces the time to search and find slides. The extracted label
text is translated into a structured
data format, stored in a database with
search capabilities. This solution has significantly saved time and effort for
pathologists by avoiding repeat sample orders, quick access to historic data,
and accuracy.
Features of Digital
Pathology
Archival/Retrieval
This platform performs the archival and
retrieval of metadata using a standard data structure.
Decision Support
This program supports determinations,
judgments, and courses of action to solve problems in decision-making
Data Harmonization
Standard structured datasets help to identify
the outliers and trends
Quality Control
Easy search and access of all the datasets
support further research and analytical activities
Remote Viewing
Easy search and access of all the datasets
support further research and analytical activities.