eprintid: 10194835
rev_number: 8
eprint_status: archive
userid: 699
dir: disk0/10/19/48/35
datestamp: 2024-07-19 07:10:50
lastmod: 2024-07-19 07:11:05
status_changed: 2024-07-19 07:10:50
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Huang, Guangyu
creators_name: Yan, Yan
creators_name: Xue, Jing-Hao
creators_name: Zhu, Wentao
creators_name: Luo, Xiongbiao
title: Interpretable Heterogeneous Teacher-Student Learning Framework for Hybrid-Supervised Pulmonary Nodule Detection
ispublished: inpress
divisions: UCL
divisions: B04
divisions: C06
divisions: F61
keywords: Heatmap learning, Hybrid-supervised learning,
Pseudo label generation, pulmonary nodule detection
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
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.
date: 2024-07-15
date_type: published
publisher: Institute of Electrical and Electronics Engineers (IEEE)
official_url: http://dx.doi.org/10.1109/tcsvt.2024.3427645
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2298146
doi: 10.1109/tcsvt.2024.3427645
lyricists_name: Xue, Jinghao
lyricists_id: JXUEX60
actors_name: Xue, Jinghao
actors_id: JXUEX60
actors_role: owner
full_text_status: public
publication: IEEE Transactions on Circuits and Systems for Video Technology
pagerange: 1-1
issn: 1051-8215
citation:        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 <https://doi.org/10.1109/tcsvt.2024.3427645>.    (In press).    Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10194835/1/GuangyuHuang-TCSVT-2024.pdf