  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 a 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 a pressing need {for} developing
label-efficient detection models to alleviate radiologists' labeling burden. To
tackle this challenge, the literature on 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 flexible and robust learning from the weakly-labeled and
unlabeled data. Extensive 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. The code is available at:
https://github.com/zhizhongchai/ORF-Net.
