Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification
This addresses the problem of reducing annotation effort in medical imaging for clinicians, though it is incremental as it builds on existing multi-instance learning and deep learning methods.
The paper tackled mammogram classification for breast cancer diagnosis by proposing deep multi-instance networks that eliminate the need for annotated regions of interest, achieving robust results on the INbreast dataset.
Mammogram classification is directly related to computer-aided diagnosis of breast cancer. Traditional methods rely on regions of interest (ROIs) which require great efforts to annotate. Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning (MIL) 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 ROIs. We explore three different schemes to construct deep multi-instance networks for whole mammogram classification. Experimental results on the INbreast dataset demonstrate the robustness of proposed networks compared to previous work using segmentation and detection annotations.