Artificial intelligence in biomedicine: A key to analyzing millions of individual cells
At the same time, the analysis is generating ever-increasing quantities of data. The researchers intend to apply machine learning methods to support the process of re-interpreting existing datasets, deriving conclusive statements from the patterns and applying the results to other areas. Self-supervised learning as a new approach Self-supervised learning is based on two methods. In masked learning -- as the name suggests -- a portion of the input data is masked and the model is trained to be able to reconstruct the missing elements. In addition, the researchers apply contrastive learning in which the model learns to combine similar data and separate dissimilar data. The team used both methods of self-supervised learning to test more than 20 million individual cells and compared them with the results of classical learning methods. In their assessment of the different methods, the…
