Artificial intelligence (AI) is being used in the evaluation of medical imaging data. This task usually requires a specially developed algorithm but scientists from the German Cancer Research Center (DKFZ) have now presented a new method for configuring self-learning algorithms for a large number of different imaging datasets – without the need for specialist knowledge or very significant computing power. Science Daily brought us this interesting information to our attention in their article, “Self-learning algorithms for different imaging datasets.”
The method, known as nnU-Net, can deal with a broad range of imaging data and can also process images from electron and fluorescence microscopy.
AI-based evaluation of medical imaging data has mainly been applied in research contexts and has not yet been broadly used in the routine clinical care of cancer patients. However, medical informatics specialists and physicians see considerable potential for its use.
These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general.
Melody K. Smith
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