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Updated: Aug 25, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Deep learning accelerates whole slide imaging for next-generation digital pathology applications.

Yair Rivenson^1,2, Aydogan Ozcan^3,4,5,6

¹Pictor Labs, Inc., Los Angeles, USA. rivenson@pictorlabs.ai.

Light, Science & Applications

|October 14, 2022

Summary

Deep learning significantly speeds up whole slide imaging in histology. This advancement accelerates the adoption of digital pathology, improving diagnostic workflows.

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Area of Science:

Histology
Digital Pathology
Artificial Intelligence

Background:

Whole slide imaging (WSI) is crucial for modern histology.
Current WSI scanning speeds can be a bottleneck in diagnostic workflows.
Digital pathology offers numerous advantages but requires efficient imaging.

Purpose of the Study:

To investigate the impact of deep learning on WSI scanning speed.
To assess the potential of deep learning to accelerate digital pathology adoption.

Main Methods:

Utilized deep learning algorithms to optimize WSI scanning parameters.
Compared scanning times of conventional methods versus deep learning-enhanced methods.

Main Results:

Deep learning significantly increased WSI scanning speed.
The proposed solution demonstrated a transformative effect on imaging efficiency.

Conclusions:

Deep learning is a viable solution for accelerating WSI acquisition.
This technology can further drive the widespread adoption of digital pathology.