The analysis of tissue samples for cancer diagnosis and treatment is still largely done under the light microscope. But researchers are now developing technologies to speed up and ultimately improve the accuracy of such diagnostics through the digitization and computer-assisted analysis of tissue biopsy images. These new technologies rely a great deal on artificial intelligence (AI) tools, which require the development and «training» of AI algorithms on large datasets of digitized whole slide images (WSIs) linked to clinical outcomes data. But images collated from multiple diagnostic laboratories can vary drastically in their quality, which can in turn compromise the training and subsequent performance of AI algorithms.
Automated assessment of pathology image quality
The analysis of tissue samples for cancer diagnosis and treatment is still largely done under the light microscope. But researchers are now developing technologies to speed up and ultimately improve the accuracy of such diagnostics through the digitization and computer-assisted analysis of tissue biopsy images. These new technologies rely a great deal on artificial intelligence (AI) tools, which require the development and "training" of AI algorithms on large datasets of digitized whole slide images (WSIs) linked to clinical outcomes data. But images collated from multiple diagnostic laboratories can vary drastically in their quality, which can in turn compromise the training and subsequent performance of AI algorithms.