Training medical image annotation models

Abstract

Techniques for training models, using weakly-labeled data, to generate predictions based on medical images are disclosed. Models can be trained to perform feature localization, object detection, and/or segmentation. Weakly-labeled data can include unlabeled data or video-level labeled data. Medical imaging data is received including a first set comprising frame-level annotations and a second set comprising weakly-labeled data. A training dataset is generated comprising frame-level ground truth data, and a model is trained, using the training dataset, to generate predictions based on new medical imaging data. In some examples, the training data set may further include weakly-labeled data. In some examples, the training procedure uses a teacher model to generate frame-level localizations (pseudo-labels), which are used to train a student model whose weights can be adaptively transferred to the teacher model. Generated predictions can include frame-level feature localizations and/or video-level annotations.

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