Self-supervised learning in medical imaging
Deep learning shows a lot of promise in healthcare, especially in medical imaging, where it can help improve the speed and accuracy of diagnosing patient conditions. But it also faces a serious barrier: The shortage of labeled training data.
In medical contexts, training data come at great costs, which makes it very difficult to use deep learning for many applications.
To overcome this hurdle, scientists have explored different solutions to various degrees of success. In a new paper, artificial intelligence researchers at Google suggest a new technique that uses self-supervised learning to train deep learning models for medical imaging. Early results show that the technique can reduce the need for annotated data and improve the performance of deep learning models in medical applications.
Read the full article on TechTalks.
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