ORF-Net: Deep Omni-supervised Rib Fracture Detection from Chest CT Scans
- URL: http://arxiv.org/abs/2207.01842v1
- Date: Tue, 5 Jul 2022 07:06:57 GMT
- Title: ORF-Net: Deep Omni-supervised Rib Fracture Detection from Chest CT Scans
- Authors: Zhizhong Chai, Huangjing Lin, Luyang Luo, Pheng-Ann Heng, and Hao Chen
- Abstract summary: radiologists need to investigate and annotate rib fractures on a slice-by-slice basis.
We propose a novel omni-supervised object detection network, which can exploit multiple different forms of annotated data.
Our proposed method outperforms other state-of-the-art approaches consistently.
- Score: 47.7670302148812
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Most of the existing object detection works are based on the bounding box
annotation: each object has a precise annotated box. However, for rib
fractures, the bounding box annotation is very labor-intensive and
time-consuming because radiologists need to investigate and annotate the rib
fractures on a slice-by-slice basis. Although a few studies have proposed
weakly-supervised methods or semi-supervised methods, they could not handle
different forms of supervision simultaneously. In this paper, we proposed a
novel omni-supervised object detection network, which can exploit multiple
different forms of annotated data to further improve the detection performance.
Specifically, the proposed network contains an omni-supervised detection head,
in which each form of annotation data corresponds to a unique classification
branch. Furthermore, we proposed a dynamic label assignment strategy for
different annotated forms of data to facilitate better learning for each
branch. Moreover, we also design a confidence-aware classification loss to
emphasize the samples with high confidence and further improve the model's
performance. Extensive experiments conducted on the testing dataset show our
proposed method outperforms other state-of-the-art approaches consistently,
demonstrating the efficacy of deep omni-supervised learning on improving rib
fracture detection performance.
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