Self-supervised learning has emerged as a powerful paradigm to bridge the gap between data abundance and label scarcity in medical imaging. By constructing supervisory signals from the data ...

In bioinformatics, using unlabeled data to augment supervised learning can reduce development costs for many machine learning (ML) applications that would otherwise require large amounts of annotation ...

Our bodies are made up of around 75 billion cells. But what function does each individual cell perform and how greatly do a healthy person's cells differ from those of someone with a disease? To draw ...

With the great success of large language models, self-supervised pre-training technologies have shown the great promise in the field of drug discovery. In particular, multimodal pre-training models ...

Self-supervised learning allows a neural network to figure out for itself what matters. The process might be what makes our own brains so successful. For a decade now, many of the most impressive ...