Cost-Sensitive Active Learning for Intracranial Hemorrhage Detection
This work addresses the high labeling costs in medical imaging for clinicians and researchers, though it is incremental as it builds on existing active learning methods.
The paper tackles the challenge of labeling medical data for intracranial hemorrhage detection by developing a cost-sensitive active learning system that optimizes labeling time and return on investment, achieving results comparable to state-of-the-art methods while being faster and using less memory on a larger dataset.
Deep learning for clinical applications is subject to stringent performance requirements, which raises a need for large labeled datasets. However, the enormous cost of labeling medical data makes this challenging. In this paper, we build a cost-sensitive active learning system for the problem of intracranial hemorrhage detection and segmentation on head computed tomography (CT). We show that our ensemble method compares favorably with the state-of-the-art, while running faster and using less memory. Moreover, our experiments are done using a substantially larger dataset than earlier papers on this topic. Since the labeling time could vary tremendously across examples, we model the labeling time and optimize the return on investment. We validate this idea by core-set selection on our large labeled dataset and by growing it with data from the wild.