An End-to-End Mammogram Diagnosis: A New Multi-Instance and Multiscale Method Based on Single-Image Feature
Zizhou Wang, Lei Zhang, Xin Shu, Qing Lv, Yi Zhang
Abstract
Mammography is the most common modality used in breast cancer detection. Most diagnostic mammography studies, however, are based on single-image training with little attention to the fact that the size of breast lesions varies significantly and the overall condition of the breast from different views. Therefore, the methodology is not in accord with the clinical requirement. To overcome this problem, we propose a new end-to-end method for mammographic diagnosis. As part of this process, we construct a data set of patients from West China Hospital to validate the new method. Furthermore, a multiscale module is proposed for the acquisition of complex breast features in a single image, enabling the screening of unique features in variably sized lesions. Finally, a multi-instance module is proposed for realistic hospital requirements to calculate the contribution of each mammogram in reaching the final diagnosis. Guidance by the single-image features can ameliorate the problem of weak one-case labeling. The new method yielded both a public data set and a realistic hospital data set.