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Communication Dans Un Congrès Année : 2018

Simultaneous Accurate Detection of Pulmonary Nodules and False Positive Reduction Using 3D CNNs

Résumé

Accurate detection of nodules in CT images is vital for lung cancer diagnosis, which greatly influences the patient's chance for survival. Motivated by successful application of convolutional neural networks (CNNs) on natural images, we propose a computer-aided diagnosis (CAD) system for both accurate pulmonary nodule detection and false positive reduction. To generate nodule candidates, we build a full 3D CNN model that employs 3D U-Net architecture as the backbone of a region proposal network (RPN). We adopt multi-task residual learning and online hard negative example mining strategy to accelerate the training process and improve the accuracy of nodule detection. Then, a 3D DenseNet-based model is presented to reduce false positive nodules. The densely connected structure reuses nodules' feature and boosts feature propagation. Experimental results on LUNA16 datasets demonstrate the superior effectiveness of our approach over state-of-the-art methods.
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Dates et versions

hal-04254078 , version 1 (26-10-2023)

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Yulei Qin, Hao Zheng, Yue-Min Zhu, Jie Yang. Simultaneous Accurate Detection of Pulmonary Nodules and False Positive Reduction Using 3D CNNs. ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr 2018, Calgary, Canada. pp.1005-1009, ⟨10.1109/ICASSP.2018.8462546⟩. ⟨hal-04254078⟩
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