Moreira da Silva D E, Gonçalves Lio, Franco-Gonçalo Pedro, Colaço Bruno, Alves-Pimenta Sofia, Ginja Mário, Ferreira Manuel, Filipe Vitor
School of Science and Technology, University of Trás-os-Montes e Alto Douro (UTAD), Vila Real, Portugal.
INESC Technology and Science (INESC TEC), Porto, Portugal.
Front Artif Intell. 2022 Oct 26;5:939967. doi: 10.3389/frai.2022.939967. eCollection 2022.
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 time complexity.
X射线骨语义分割是医学成像中的一项关键任务。由于深度学习的出现，构建高精度模型成为可能。然而，这些模型需要大量的标注数据。此外，语义分割需要逐像素标注，因此是一项非常耗时的任务。就髋关节而言，由于股骨和髋臼的内在特性，对解剖学知识的需求仍然很大。主动学习旨在用尽可能少的数据量最大化模型的性能。在这项工作中，我们提出并比较了不同查询的使用，包括基于不确定性和多样性的查询。我们的结果表明，所提出的方法仅使用81.02%的数据就能实现领先的性能，且具有时间复杂度。
