Deep learning (DL)-based rib fracture detection has shown promise of playing an important role in preventing mortality and improving patient outcome. Normally, developing DL-based object detection models requires huge amount of bounding box annotation. However, annotating medical data is time-consuming and expertise-demanding, making obtaining a large amount of fine-grained annotations extremely infeasible. This poses pressing need of developing label-efficient detection models to alleviate radiologists' labeling burden. To tackle this challenge, the literature of object detection has witnessed an increase of weakly-supervised and semi-supervised approaches, yet still lacks a unified framework that leverages various forms of fully-labeled, weakly-labeled, and unlabeled data. In this paper, we present a novel omni-supervised object detection network, ORF-Netv2, to leverage as much available supervision as possible. Specifically, a multi-branch omni-supervised detection head is introduced with each branch trained with a specific type of supervision. A co-training-based dynamic label assignment strategy is then proposed to enable flexibly and robustly learning from the weakly-labeled and unlabeled data. Extensively evaluation was conducted for the proposed framework with three rib fracture datasets on both chest CT and X-ray. By leveraging all forms of supervision, ORF-Netv2 achieves mAPs of 34.7, 44.7, and 19.4 on the three datasets, respectively, surpassing the baseline detector which uses only box annotations by mAP gains of 3.8, 4.8, and 5.0, respectively. Furthermore, ORF-Netv2 consistently outperforms other competitive label-efficient methods over various scenarios, showing a promising framework for label-efficient fracture detection.

翻译：基于深度学习的肋骨骨折检测在降低死亡率、改善患者预后方面展现出重要潜力。通常，开发基于深度学习的目标检测模型需要大量边界框标注。然而，医学数据标注耗时长且需要专业知识，使得获取大规模细粒度标注极不可行。这迫切要求开发标签高效的检测模型以减轻放射科医生的标注负担。为应对这一挑战，目标检测领域涌现出大量弱监督与半监督方法，但仍缺乏一个统一框架来充分利用全标注、弱标注及无标注数据。本文提出一种新颖的全监督目标检测网络ORF-Netv2，旨在最大化利用可用监督信息。具体而言，我们引入多分支全监督检测头，每个分支针对特定监督类型进行训练；随后提出基于协同训练的动态标签分配策略，以实现对弱标注和无标注数据的灵活稳健学习。我们在三个肋骨骨折数据集（涵盖胸部CT与X光片）上对该框架进行了广泛评估。通过利用所有形式的监督信息，ORF-Netv2在三个数据集上分别实现了34.7、44.7和19.4的mAP值，相比仅使用框标注的基线检测器分别提升了3.8、4.8和5.0个mAP。此外，ORF-Netv2在不同场景中始终优于其他标签高效竞争方法，展现出用于标签高效骨折检测的前景性框架。