Huang, Guangyu;
Yan, Yan;
Xue, Jing-Hao;
Zhu, Wentao;
Luo, Xiongbiao;
(2024)
Interpretable Heterogeneous Teacher-Student Learning Framework for Hybrid-Supervised Pulmonary Nodule Detection.
IEEE Transactions on Circuits and Systems for Video Technology
p. 1.
10.1109/tcsvt.2024.3427645.
(In press).
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Abstract
Existing pulmonary nodule detection methods often train models in a fully-supervised setting that requires strong labels (i.e., bounding box labels) as label information. However, manual annotation of bounding boxes in CT images is very time-consuming and labor-intensive. To alleviate the annotation burden, in this paper, we investigate pulmonary nodule detection by leveraging both strong labels and weak labels (i.e., center point labels) for training, and propose a novel hybrid-supervised pulmonary nodule detection (HND) method. The training of HND involves a heterogeneous teacher-student learning framework in two stages. In the first stage, we design a point-based consistency calibration network (PCC-Net) as a teacher, which is pre-trained to generate high-quality pseudo bounding box labels given point-augmented CT images as inputs. In the second stage, we develop an information bottleneck-guided pulmonary nodule detection network (IBD-Net) as a student to perform pulmonary nodule detection. In particular, we introduce information bottleneck to learn reliable pulmonary nodule-specific heatmaps under the guidance of PCC-Net, largely enhancing the model’s interpretability and improving the final detection performance. Based on the above designs, our method can effectively detect pulmonary nodule regions with only a limited number of bounding box labels. Experimental results on the public pulmonary nodule detection dataset LUNA16 show that our HND method achieves an excellent balance between the annotation cost and the detection performance.
| Type: | Article |
|---|---|
| Title: | Interpretable Heterogeneous Teacher-Student Learning Framework for Hybrid-Supervised Pulmonary Nodule Detection |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/tcsvt.2024.3427645 |
| Publisher version: | http://dx.doi.org/10.1109/tcsvt.2024.3427645 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Heatmap learning, Hybrid-supervised learning, Pseudo label generation, pulmonary nodule detection |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10194835 |
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