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.

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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.