Deep learning accurately stains digital biopsy slides

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Deep learning accurately stains digital biopsy slides
Deep learning accurately stains digital biopsy slides

Deep learning accurately stains digital biopsy slides

Deep learning accurately stains digital biopsy slides. A research team led by MIT scientists at the Media Lab, in collaboration with clinicians at Stanford University School of Medicine and Harvard Medical School, now shows that digital scans of these biopsy slides can be stained computationally, using deep learning algorithms trained on data from physically dyed slides.

This process of computational digital staining and de-staining preserves small amounts of tissue biopsied from cancer patients and allows researchers and clinicians to analyze slides for multiple kinds of diagnostic and prognostic tests, without needing to extract additional tissue sections.

The researchers also analyzed the steps by which the deep learning neural networks stained the slides, which is key for clinical translation of these deep learning systems, says Pratik Shah, MIT principal research scientist and the study’s senior author.

“The problem is tissue, the solution is an algorithm, but we also need ratification of the results generated by these learning systems,” he says. “This provides explanation and validation of randomized clinical trials of deep learning models and their findings for clinical applications.”

The pathologists’ assessments from the computationally stained slides also agreed with majority of the initial clinical diagnoses included in the patient’s electronic health records. In two cases, the computationally stained images overturned the original diagnoses, the researchers found.

Another important part of the study involved using novel methods to visualize and explain how the neural networks assembled computationally stained and de-stained images. This was done by creating a pixel-by-pixel visualization and explanation of the process using activation maps of neural network models corresponding to tumors and other features used by clinicians for differential diagnoses.

This type of analysis helps to create a verification process that is needed when evaluating “software as a medical device,” says Shah, who is working with the U.S. Food and Drug Administration on ways to regulate and  translate computational medicine for clinical applications.

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