Self-Learning Algorithms For Different Imaging Datasets

AI-based evaluation of medical imaging data usually requires a specially developed algorithm for each task. Scientists 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.

The method, known as nnU-Net, can deal with a broad range of imaging data: in addition to conventional imaging methods such as CT and MRI, it can also process images from electron and fluorescence microscopy.

So far, 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 self-learning algorithms, for example for highly repetitive tasks, such as those that often need to be performed as part of large-scale clinical studies. nnU-Net can help harness this potential.

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