Semi-supervised training using cooperative labeling of weakly annotated data for nodule detection in chest CT

Machine learning algorithms are best trained with large quantities of accurately annotated samples. While natural scene images can often be labeled relatively cheaply and at large scale, obtaining accurate annotations for medical images is both time consuming and expensive. In this study, we propose a cooperative labeling method that allows us to make use of weakly annotated medical imaging data for the training of a machine learning algorithm. As most clinically produced data are weakly-annotated – produced for use by humans rather than machines and lacking information machine learning depends upon – this approach allows us to incorporate a wider range of clinical data and thereby increase the training set size.
Read more here: https://aapm.onlinelibrary.wiley.com/doi/abs/10.1002/mp.16219