Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification

Understand

Mammogram classification is directly related to computer-aided diagnosis of breast cancer.

• Traditional methods requires great effort to annotate the training data by costly manual labeling and specialized computational models to detect these annotations during test.
• Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning for labeling a set of instances/patches, we propose end-to-end trained deep multi-instance networks for mass classification based on whole mammogram without the aforementioned costly need to annotate the training data.
• We explore three different schemes to construct deep multi-instance networks for whole mammogram classification.