Annotation-cost Minimization for Medical Image Segmentation using Suggestive Mixed Supervision Fully Convolutional Networks
This addresses the annotation burden in medical imaging, offering a cost-effective solution for segmentation tasks, though it is incremental as it builds on existing mixed-supervision approaches.
The paper tackles the high annotation cost for medical image segmentation by proposing a budget-based framework that uses mixed supervision (dense segmentations, bounding boxes, landmarks) and a linear programming formulation with ranking to select samples for annotation. It achieves comparable performance to state-of-the-art methods with significantly reduced annotation costs.
For medical image segmentation, most fully convolutional networks (FCNs) need strong supervision through a large sample of high-quality dense segmentations, which is taxing in terms of costs, time and logistics involved. This burden of annotation can be alleviated by exploiting weak inexpensive annotations such as bounding boxes and anatomical landmarks. However, it is very difficult to \textit{a priori} estimate the optimal balance between the number of annotations needed for each supervision type that leads to maximum performance with the least annotation cost. To optimize this cost-performance trade off, we present a budget-based cost-minimization framework in a mixed-supervision setting via dense segmentations, bounding boxes, and landmarks. We propose a linear programming (LP) formulation combined with uncertainty and similarity based ranking strategy to judiciously select samples to be annotated next for optimal performance. In the results section, we show that our proposed method achieves comparable performance to state-of-the-art approaches with significantly reduced cost of annotations.