Active learning for data efficient semantic segmentation of canine bones in radiographs

Abstract

X-ray bone semantic segmentation is one crucial task in medical imaging. Due to deep learning's emergence, it was possible to build high-precision models. However, these models require a large quantity of annotated data. Furthermore, semantic segmentation requires pixel-wise labeling, thus being a highly time-consuming task. In the case of hip joints, there is still a need for increased anatomic knowledge due to the intrinsic nature of the femur and acetabulum. Active learning aims to maximize the model's performance with the least possible amount of data. In this work, we propose and compare the use of different queries, including uncertainty and diversity-based queries. Our results show that the proposed methods permit state-of-the-art performance using only 81.02% of the data, with $O\left(1\right)$ time complexity.

Keywords: Monte Carlo Dropout sampling; Shannon's entropy; X-ray image analysis; active learning; cluster based sampling; deep learning; representative sampling.