Group-Attention Single-Shot Detector (GA-SSD): Finding Pulmonary Nodules in Large-Scale CT Images

Jiechao Ma, Xiang Li, Hongwei Li, Bjoern H. Menze, Sen Liang, Rongguo Zhang, Wei-Shi Zheng
Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning, PMLR 102:358-369, 2019.

Abstract

Early diagnosis of pulmonary nodules (PNs) can improve the survival rate of patients and yet is a challenging task for radiologists due to the image noise and artifacts in computed tomography (CT) images. In this paper, we propose a novel and effective abnormality detector implementing the attention mechanism and group convolution on 3D single-shot detector (SSD) called group-attention SSD (GA-SSD). We find that group convolution is effective in extracting rich context information between continuous slices, and attention network can learn the target features automatically. We collected a large-scale dataset that contained 4146 CT scans with annotations of varying types and sizes of PNs (even PNs smaller than 3mm). To the best of our knowledge, this dataset is the largest cohort with relatively complete annotations for PNs detection. Extensive experimental results show that the proposed group-attention SSD outperforms the conventional SSD framework as well as the state-of-the-art 3DCNN, especially on some challenging lesion types.

Cite this Paper

BibTeX
@InProceedings{pmlr-v102-ma19a, title = {Group-Attention Single-Shot Detector (GA-SSD): Finding Pulmonary Nodules in Large-Scale CT Images}, author = {Ma, Jiechao and Li, Xiang and Li, Hongwei and Menze, Bjoern H. and Liang, Sen and Zhang, Rongguo and Zheng, Wei-Shi}, booktitle = {Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning}, pages = {358--369}, year = {2019}, editor = {Cardoso, M. Jorge and Feragen, Aasa and Glocker, Ben and Konukoglu, Ender and Oguz, Ipek and Unal, Gozde and Vercauteren, Tom}, volume = {102}, series = {Proceedings of Machine Learning Research}, month = {08--10 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v102/ma19a/ma19a.pdf}, url = {https://proceedings.mlr.press/v102/ma19a.html}, abstract = {Early diagnosis of pulmonary nodules (PNs) can improve the survival rate of patients and yet is a challenging task for radiologists due to the image noise and artifacts in computed tomography (CT) images. In this paper, we propose a novel and effective abnormality detector implementing the attention mechanism and group convolution on 3D single-shot detector (SSD) called group-attention SSD (GA-SSD). We find that group convolution is effective in extracting rich context information between continuous slices, and attention network can learn the target features automatically. We collected a large-scale dataset that contained 4146 CT scans with annotations of varying types and sizes of PNs (even PNs smaller than 3mm). To the best of our knowledge, this dataset is the largest cohort with relatively complete annotations for PNs detection. Extensive experimental results show that the proposed group-attention SSD outperforms the conventional SSD framework as well as the state-of-the-art 3DCNN, especially on some challenging lesion types.} }
Endnote
%0 Conference Paper %T Group-Attention Single-Shot Detector (GA-SSD): Finding Pulmonary Nodules in Large-Scale CT Images %A Jiechao Ma %A Xiang Li %A Hongwei Li %A Bjoern H. Menze %A Sen Liang %A Rongguo Zhang %A Wei-Shi Zheng %B Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2019 %E M. Jorge Cardoso %E Aasa Feragen %E Ben Glocker %E Ender Konukoglu %E Ipek Oguz %E Gozde Unal %E Tom Vercauteren %F pmlr-v102-ma19a %I PMLR %P 358--369 %U https://proceedings.mlr.press/v102/ma19a.html %V 102 %X Early diagnosis of pulmonary nodules (PNs) can improve the survival rate of patients and yet is a challenging task for radiologists due to the image noise and artifacts in computed tomography (CT) images. In this paper, we propose a novel and effective abnormality detector implementing the attention mechanism and group convolution on 3D single-shot detector (SSD) called group-attention SSD (GA-SSD). We find that group convolution is effective in extracting rich context information between continuous slices, and attention network can learn the target features automatically. We collected a large-scale dataset that contained 4146 CT scans with annotations of varying types and sizes of PNs (even PNs smaller than 3mm). To the best of our knowledge, this dataset is the largest cohort with relatively complete annotations for PNs detection. Extensive experimental results show that the proposed group-attention SSD outperforms the conventional SSD framework as well as the state-of-the-art 3DCNN, especially on some challenging lesion types.
APA
Ma, J., Li, X., Li, H., Menze, B.H., Liang, S., Zhang, R. & Zheng, W.. (2019). Group-Attention Single-Shot Detector (GA-SSD): Finding Pulmonary Nodules in Large-Scale CT Images. Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 102:358-369 Available from https://proceedings.mlr.press/v102/ma19a.html.

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