In Toxicologic pathology
Digitization of histologic slides brings with it the promise of enhanced toxicologic pathology practice through the increased application of computational methods. However, the development of these advanced methods requires access to substrate image data, that is, whole slide images (WSIs). Deep learning methods, in particular, rely on extensive training data to develop robust algorithms. As a result, pharmaceutical companies interested in leveraging computational methods in their digital pathology workflows must first invest in data infrastructure to enable data access for both data scientists and pathologists. The process of building robust image data resources is challenging and includes considerations of generation, curation, and storage of WSI files, and WSI access including via linked metadata. This opinion piece describes the collective experience of building resources for WSI data in the Roche group. We elaborate on the challenges encountered and solutions developed with the goal of providing examples of how to build a data resource for digital pathology analytics in the pharmaceutical industry.
Ge Xing-Yue, Funk Juergen, Albrecht Tom, Birkhimer Merima, Gilsdorf Moritz, Hayes Matthew, Hu Fangyao, Maliver Pierre, McCreary Mark, Nguyen Trung, Romero-Palomo Fernando, Seger Shanon, Fuji Reina N, Schumacher Vanessa, Sullivan Ruth
2022-Nov-05
FAIR data, digital pathology, image data management, pharmaceutical industry, slide scanning, whole slide images
