ORF-Net: Deep Omni-supervised Rib Fracture Detection from Chest CT Scans

TL;DR

This paper introduces ORF-Net, a deep omni-supervised detection model that leverages various annotation types to improve rib fracture detection in chest CT scans, reducing annotation effort and enhancing accuracy.

Contribution

The paper presents a novel omni-supervised detection network with multiple classification branches and a dynamic label assignment strategy for improved rib fracture detection.

Findings

Outperforms state-of-the-art methods on testing dataset

Effectively utilizes multiple annotation forms

Improves detection accuracy and robustness

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.