Weak supervision setting Sample Clauses

Weak supervision setting. In addition to the above unsupervised methods, we explore a semi-supervised setting where we have a dataset for which only exam-level labels are available. In particular there means that in at least one of the four images of the exam, consisting of the CC and and MLO view of both the left and right breast, there is a lesion, but without precise location which is information which is usually readily available. We propose to use these “weak” labels to class balance batches with pseudo-labeling. To do this, during training, we use the network to predict the labels for patches only from positive exams. Specifically, per exam only the top four candidates are used as positives, while the rest is discarded. This choice is motivated by the domain knowledge: most lesions have at least 2 candidates and a lesion is visible in both views of that breast. This technique removes a lot of false positives coming from negative cases, at the cost of needing additional information.
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