Globally-Aware Multiple Instance Classifier for Breast Cancer Screening.

TitleGlobally-Aware Multiple Instance Classifier for Breast Cancer Screening.
Publication TypeJournal Article
Year of Publication2019
AuthorsShen Y, Wu N, Phang J, Park J, Kim G, Moy L, Cho K, Geras KJ
JournalMach Learn Med Imaging
Volume11861
Pagination18-26
Date Published2019 Oct
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.

DOI10.1007/978-3-030-32692-0_3
Alternate JournalMach Learn Med Imaging
PubMed ID32149282
PubMed Central IDPMC7060084
Grant ListP41 EB017183 / EB / NIBIB NIH HHS / United States
R21 CA225175 / CA / NCI NIH HHS / United States
Related Institute: 
MRI Research Institute (MRIRI)

Weill Cornell Medicine
Department of Radiology
525 East 68th Street New York, NY 10065