Joint Weakly and Semi-Supervised Deep Learning for Localization and Classification of Masses in Breast Ultrasound Images

Understand

We propose a framework for localization and classification of masses in breast ultrasound (BUS) images.

• We have experimentally found that training convolutional neural network based mass detectors with large, weakly annotated datasets presents a non-trivial problem, while overfitting may occur with those trained with small, strongly annotated datasets.
• To overcome these problems, we use a weakly annotated dataset together with a smaller strongly annotated dataset in a hybrid manner.
• We propose a systematic weakly and semi-supervised training scenario with appropriate training loss selection.