Globally-Aware Multiple Instance Classifier for Breast Cancer Screening.

Publication Type	Academic Article
Authors	Shen Y, Wu N, Phang J, Park J, Kim G, Moy L, Cho K, Geras K
Journal	Mach Learn Med Imaging
Volume	11861
Pagination	18-26
Date Published	10/10/2019
Abstract	Deep learning models designed for visual classification tasks on natural images have become prevalent in medical image analysis. However, medical images differ from typical natural images in many ways, such as significantly higher resolutions and smaller regions of interest. Moreover, both the global structure and local details play important roles in medical image analysis tasks. To address these unique properties of medical images, we propose a neural network that is able to classify breast cancer lesions utilizing information from both a global saliency map and multiple local patches. The proposed model outperforms the ResNet-based baseline and achieves radiologist-level performance in the interpretation of screening mammography. Although our model is trained only with image-level labels, it is able to generate pixel-level saliency maps that provide localization of possible malignant findings.
DOI	10.1007/978-3-030-32692-0_3
PubMed ID	32149282
PubMed Central ID	PMC7060084