In this paper, we introduce the FOCAL (Ford-OLIVES Collaboration on Active Learning) dataset which enables the study of the impact of annotation-cost within a video active learning setting.
In recent years, deep neural networks have significantly impacted the seismic interpretation process.
To alleviate this issue, we propose a grounded second-order definition of information content and sample importance within the context of active learning.
However, existing strategies directly base the data selection on the data representation of the unlabeled data which is random for OOD samples by definition.
Inspired by this phenomenon, we present a novel segmentation method that actively uses this information to alter the data representation within the model by increasing the variety of difficult regions.
For this purpose, we propose a framework that incorporates clinical insights into the sample selection process of active learning that can be incorporated with existing algorithms.