Title | Globally-Aware Multiple Instance Classifier for Breast Cancer Screening. |
Publication Type | Journal Article |
Year of Publication | 2019 |
Authors | Shen Y, Wu N, Phang J, Park J, Kim G, Moy L, Cho K, Geras KJ |
Journal | Mach Learn Med Imaging |
Volume | 11861 |
Pagination | 18-26 |
Date Published | 2019 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. |
DOI | 10.1007/978-3-030-32692-0_3 |
Alternate Journal | Mach Learn Med Imaging |
PubMed ID | 32149282 |
PubMed Central ID | PMC7060084 |
Grant List | P41 EB017183 / EB / NIBIB NIH HHS / United States R21 CA225175 / CA / NCI NIH HHS / United States |
Related Institute:
MRI Research Institute (MRIRI)