LABELING COST SENSITIVE BATCH ACTIVE LEARNING FOR BRAIN TUMOR SEGMENTATION

Over the last decade, deep learning methods have achieved state-of-the-art for medical image segmentation tasks. However, the difficulty of obtaining sufficient labeled data can be a bottleneck. To this end, we design a novel active learning framework specially adapted to the brain tumor segmentation. Our approach includes a novel labeling cost designed to capture radiologists' practical labeling costs. This is combined with two acquisition functions to incorporate uncertainty and representation information, ensuring that the active learning selects informative and diverse data. The resulting procedure is a constrained combinatorial optimization problem. We propose an efficient algorithm for this task and demonstrate the proposed method's advantages for segmenting brain MRI data.