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.
Self-supervised learning in medical imaging
Self-supervised learning in medical imaging
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.