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Found 5 papers, 1 papers with code
Deep Rib Fracture Instance Segmentation and Classification from CT on the RibFrac Challenge
The resulting FracNet+ demonstrates competitive performance in rib fracture detection, which lays a foundation for further research and development in AI-assisted rib fracture detection and diagnosis.
Deep Omni-supervised Learning for Rib Fracture Detection from Chest Radiology Images
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.
ORF-Net: Deep Omni-supervised Rib Fracture Detection from Chest CT Scans
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.
Conquering Data Variations in Resolution: A Slice-Aware Multi-Branch Decoder Network
Based on the aforementioned innovations, we achieve state-of-the-art results on the MICCAI 2017 Liver Tumor Segmentation (LiTS) dataset.
Deep Semi-supervised Metric Learning with Dual Alignment for Cervical Cancer Cell Detection
Specifically, our model learns a metric space and conducts dual alignment of semantic features on both the proposal level and the prototype levels.
