  Automatic methods for early detection of breast cancer on mammography can
significantly decrease mortality. Broad uptake of those methods in hospitals is
currently hindered because the methods have too many constraints. They assume
annotations available for single images or even regions-of-interest (ROIs), and
a fixed number of images per patient. Both assumptions do not hold in a general
hospital setting. Relaxing those assumptions results in a weakly supervised
learning setting, where labels are available per case, but not for individual
images or ROIs. Not all images taken for a patient contain malignant regions
and the malignant ROIs cover only a tiny part of an image, whereas most image
regions represent benign tissue. In this work, we investigate a two-level
multi-instance learning (MIL) approach for case-level breast cancer prediction
on two public datasets (1.6k and 5k cases) and an in-house dataset of 21k
cases. Observing that breast cancer is usually only present in one side, while
images of both breasts are taken as a precaution, we propose a domain-specific
MIL pooling variant. We show that two-level MIL can be applied in realistic
clinical settings where only case labels, and a variable number of images per
patient are available. Data in realistic settings scales with continuous
patient intake, while manual annotation efforts do not. Hence, research should
focus in particular on unsupervised ROI extraction, in order to improve breast
cancer prediction for all patients.
