Case-level Breast Cancer Prediction for Real Hospital Settings
This addresses the challenge of scalable breast cancer prediction for hospitals by enabling training with readily available case labels, though it is incremental in adapting existing MIL methods to a specific clinical bottleneck.
The paper tackles the problem of breast cancer prediction in real hospital settings where only case-level diagnoses are available, by developing a two-level multi-instance learning framework that eliminates the need for manual annotations and handles variable numbers of images per case. The result shows no loss in performance compared to models trained on image labels, while also localizing abnormalities in breast side, view, and region.
Breast cancer prediction models for mammography assume that annotations are available for individual images or regions of interest (ROIs), and that there is a fixed number of images per patient. These assumptions do not hold in real hospital settings, where clinicians provide only a final diagnosis for the entire mammography exam (case). Since data in real hospital settings scales with continuous patient intake, while manual annotation efforts do not, we develop a framework for case-level breast cancer prediction that does not require any manual annotation and can be trained with case labels readily available at the hospital. Specifically, we propose a two-level multi-instance learning (MIL) approach at patch and image level for case-level breast cancer prediction and evaluate it on two public and one private dataset. We propose a novel domain-specific MIL pooling observing that breast cancer may or may not occur in both sides, while images of both breasts are taken as a precaution during mammography. We propose a dynamic training procedure for training our MIL framework on a variable number of images per case. We show that our two-level MIL model can be applied in real hospital settings where only case labels, and a variable number of images per case are available, without any loss in performance compared to models trained on image labels. Only trained with weak (case-level) labels, it has the capability to point out in which breast side, mammography view and view region the abnormality lies.