What is it about?
Recent developments in digital slide scanner technologies and the need for optimizing histopathologists workflow have led to the rise of digital pathology. Along with digital pathology, artificial intelligence assisted diagnoses are foreseen as the way to further enhance pathologists’ productivity and therefore support them with their ever-growing workload. Although imaging technologies can be digitalized, most of the slide preparation is still a manual process which lacks any sort of standardization. For this reason, digital images appearance is dependent on the center where the slide is prepared and artificial intelligence algorithms will perform poorly on slides from previously unseen medical centers. In this work, we introduce a method, based on transfer learning, for adapting artificial intelligence algorithms to new medical centers.
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Photo by National Cancer Institute on Unsplash
Why is it important?
We show that the method allows for algorithm performance preservation with reduced need for training data. This is a first step towards building artificial intelligence algorithms with good performance on every medical center.
Perspectives
I hope this article convinces readers that the challenge posed by slide preparation variability can be solved.
Rémy Peyret
Primaa
With this transdisciplinary article, a further step towards the routine implementation of artificial intelligence in histology is accomplished.
Stephane Sockeel
Primaa
Read the Original
This page is a summary of: Multicenter automatic detection of invasive carcinoma on breast whole slide images, PLOS Digital Health, February 2023, PLOS,
DOI: 10.1371/journal.pdig.0000091.
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